Iron (Fe) is an essential micronutrient for phytoplankton, particularly diazotrophs, which are abundant in the Western Tropical South Pacific Ocean (WTSP). Their success depends on the numerous trace metals, particularly iron, released from shallow hydrothermal vents along the Tonga Arc. This study aimed to explore the impact of hydrothermal fluids on particulate trace metal concentrations and biological activity. To identify the composition of sinking particles across a wide area of the WTSP, we deployed sediment traps at various depths, both close and further west of the Tonga Arc. Seafloor sediments were cored at these deployment sites, including at a remote location in the South Pacific Gyre. The sinking particles were composed of a large amount of biological material, indicative of the high productivity of the Lau Basin. A significant portion of this material was lithogenic of hydrothermal origin, as revealed through Al-Fe-Mn tracing. The sinking material showed similar patterns between lithogenic and biogenic fractions, indicating that hydrothermal input within the photic layer triggered surface production. A hydrothermal fingerprint was suggested in the sediments due to the high sedimentation rates and the presence of large, heterogeneous, trace metal-rich particles. The presence of nearby active deep hydrothermal sources was suspected near the Lau Ridge due to the large particle size and the significant enrichment of Fe and Mn. Overall, this study revealed that deep and shallow hydrothermal sources along with submarine volcanism have a significant influence on the biogeochemical signature of particles in the Lau Basin at large spatial and temporal scales.

Land use and land cover change (LULCC) represents a key process of human-Earth system interaction and has profound impacts on ecosystem carbon cycling. As a key input for ecosystem models, future gridded LULCC data is typically spatially downscaled from regionally LULCC projections by integrated assessment models. The uncertainty associated with different spatial downscaling methods and its impacts on subsequent model projections have been historically ignored and rarely examined. This study investigated this problem using two representative spatial downscaling methods and focused on the impacts on the carbon cycle over ABoVE domain. Specifically, we used the Future Land Use Simulation model (FLUS) and Demeter model to generate 0.25-degree gridded LULCC data with the same input of regional LULCC projections from Global Change Analysis Model, under SSP126 and SSP585. The two sets of downscaled LULCC were used to drive CLM5 to prognostically simulate terrestrial carbon cycle dynamics over the 21st century. The results suggest large spatial-temporal differences between two LULCC datasets under both SSP126 and SSP585. The LULCC differences further lead to large discrepancies in the spatial patterns of projected carbon cycle variables, which are more than 79% of the contributions of LULCC in 2100. Besides, the difference for LULCC and carbon flux under SSP126 is generally larger than those under SSP585. This study highlights the importance of considering the uncertainties induced by spatial downscaling process in future LULCC projections and carbon cycle simulations.

The Sustainable Development Goals (SDGs) provide targets for humanity to achieve sustainable development by 2030. A monitoring framework of 248 environmental, social, and economic indicators, reported nationally by 193 UN Member States, tracks progress. The framework includes 92 environmental indicators, most of which refer to environmental policies. The SDG monitoring framework provides data to assess whether, across countries, environmental policies are: 1. Addressing environmental pressures, 2. Linked to environmental improvements, and 3. Linked with societal benefits delivered by healthy environments. We use statistical analysis and a generalized linear modeling approach to test for correlations between SDG indicators related to environmental policies, environmental pressures, the state of the environment, and social impacts delivered by healthy environments. Our results show that environmental policies, particularly protected areas and sustainable forest certification, are linked with environmental improvements, mainly in forest and water ecosystems. However, we find no evidence that environmental improvements are linked with positive social impacts. Finally, environmental pressures, including freshwater withdrawal, domestic material consumption, and tourism, are linked with environmental degradation. Environmental policy responses are generally increasing across countries. Despite this, the state of the environment globally continues to decline. Governments must focus on understanding why environmental policies have not been sufficient to reverse environmental decline, particularly concerning the pressures that continue to degrade the environment. To better track progress towards sustainable development, we recommend that the SDG monitoring framework is supplemented with additional indicators on the state of the environment.

Fire is a crucial factor in terrestrial ecosystems playing a role in disturbance for vegetation dynamics. Process-based fire models quantify fire disturbance effects in stand-alone dynamic global vegetation models (DGVMs) and their advances have incorporated both descriptions of natural processes and anthropogenic drivers. Nevertheless, these models show limited skill in modeling fire events at the global scale, due to stochastic characteristics of fire occurrence and behavior as well as the limits in empirical parameterizations in process-based models. As an alternative, machine learning has shown the capability of providing robust diagnostics of fire regimes. Here, we develop a deep-learning-based fire model (DL-fire) to estimate daily burnt area fraction at the global scale and couple it within JSBACH4, the land surface model used in the ICON ESM. The stand-alone DL-fire model forced with meteorological, terrestrial and socio-economic variables is able to simulate global total burnt area, showing 0.8 of monthly correlation (rm) with GFED4 during the evaluation period (2011-15). The performance remains similar with the hybrid modeling approach JSB4-DL-fire (rm=0.79) outperforming the currently used uncalibrated standard fire model in JSBACH4 (rm=-0.07). We further quantify the importance of each predictor by applying layer-wise relevance propagation (LRP). Overall, land properties, such as fuel amount and water content in soil layers, stand out as the major factors determining burnt fraction in DL-fire, paralleled by meteorological conditions over tropical and high latitude regions. Our study demonstrates the potential of hybrid modeling in advancing fire prediction in ESMs by integrating deep learning approaches in physics-based dynamical models.

InSight’s seismometers recorded more than 1300 events. Ninety-eight of these, named the low-frequency family, show energy predominantly below 1 Hz down to ∼0.125 Hz. The Marsquake Service identified seismic phases and computed distances for 42 of these marsquakes, 26 of which have backazimuths. Hence, the locations of the majority of low-frequency family events remain undetermined. Here, we use an envelope shape similarity approach to determine event classes and distances, and introduce an alternative method to estimate the backazimuth. In our similarity approach, we use the highest quality marsquakes with well-constrained distance estimates as templates, including the largest event S1222a, and assign distances to marsquakes with relatively high signal-to-noise ratio based on their similarities to the template events. The resulting enhanced catalog allows us to re-evaluate the seismicity of Mars. We find the Valles Marineris region to be more active than initially perceived, where only a single marsquake (S0976a) had previously been located. We relocated two marsquakes using new backazimuth estimates, which had reported distances of ∼90o, in the SW of the Tharsis region, possibly at Olympus Mons. In addition, two marsquakes with little or no S-wave energy have been located in the NE of the Elysium Bulge. Event epicenters in Cerberus Fossae follow a North-South trend due to uncertainties in location, while the fault system is in the NW-SE direction; therefore, these events are re-projected along the observed fault system.

Using a three-dimensional coupled physical-biological model, this paper explores the creation of phytoplankton blooms around tropical islands in the presence of ambient currents and short-lived (~4 days) wind events. The ambient flow creates a retention zone of weak flows in the lee of the island, which is a typical feature of island wakes. Findings reveal that wind-induced upwelling effects are essential for the initial nutrient enrichment and phytoplankton growth that occur mainly in this retention zone. Oscillating flow, typical of island wakes, occasionally releases mesoscale patches of upwelled water and its phytoplankton load into the ambient ocean. The phytoplankton continues to grow within floating structures that are of up to 20 km in diameter. This mechanism complements the plankton growth associated with the formation of mesoscale eddies.

Dynamical models used in climate prediction often have systematic errors that can deteriorate predictions. In this study, we work in a twin experiment framework with a reduced-order coupled ocean-atmosphere model and aim to demonstrate the benefit of machine learning for climate prediction. Machine learning is applied to learn the model error and thus build a data-driven model to emulate the dynamical model error. Then we build a hybrid model by combining the data-driven and dynamical models. The prediction skill of the hybrid model is compared to that of the standalone dynamical model. We applied this approach to the ocean-atmosphere coupled model. The results show that the hybrid model outperforms the dynamical model alone for both atmospheric and oceanic variables. Also, we build two other hybrid models only correcting either atmospheric errors or oceanic errors. It was found that correcting both atmospheric and oceanic errors leads to the best performance.

Mechanical properties of living cells play an important role in helping to understand cell physiology and pathology. However, to ensure reliability, it is important to precisely define the experimental conditions of these mechanical test. In this study, viscoelastic properties of the outer layer (cytoplasm and membrane) were extracted using standard linear solid model. Finite element modeling of the two-layer cell was performed and the model was validated by experimental data. In the two-layer model, the effect of the radius of the nucleus and the location of the nucleus on the whole cell behavior were investigated. By increasing nucleus size, the whole cell properties follow the nucleus properties and the role of the nucleus becomes more dominant. By dislocating nucleus inside the cytoplasm, the cell behavior changes significantly and becomes more similar to that of nucleus properties. Similarly, changing the aspiration pressure and rate, impacts the observed behavior drastically. The results demonstrate that the location of the nucleus and the ratio of the radius of the cytoplasm to the radius of the nucleus can effectively influence the viscoelastic properties and mechanical behavior of the cell.

Purpose: With the expansion of research utilizing electronic healthcare data to identify transgender (TG) population health trends, the validity of computational phenotype algorithms to identify TG patients is not well understood. We aim to identify the current state of the literature that has utilized CPs to identify TG people within electronic healthcare data and their validity, potential gaps, and a synthesis of future recommendations based on past studies. Methods: Authors searched the National Library of Medicine’s PubMed, Scopus, and the American Psychological Association Psyc Info’s databases to identify studies published in the United States that applied CPs to identify TG people within electronic health care data. Results: Twelve studies were able to validate or enhance the positive predictive value (PPV) of their CP through manual chart reviews (n=5), hierarchy of code mechanisms (n=4), key text-strings (n=2), or self-surveys (n=1). CPs with the highest PPV to identify TG patients within their study population contained diagnosis codes and other components such as key text-strings. However, if key text-strings were not available, researchers have been able to find most TG patients within their electronic healthcare databases through diagnosis codes alone. Conclusion: CPs with the highest accuracy to identify TG patients contained diagnosis codes along with components such as procedural codes or key text-strings. CPs with high validity are essential to identifying TG patients when self-reported gender identity is not available. Still, self-reported gender identity information should be collected within electronic healthcare data as it is the gold standard method to better understand TG population health patterns.

Evaluating Serum HE4: Some Serious ConsiderationsAimen Waqar Khana, Hussain Haider Shahba: Department of Medicine, Jinnah Sindh Medical University, Karachi, Pakistan.b: Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan.Dear Dr Papageorghiou,We have perused with great interest the scholarly article ”Serum HE4 predicts progestin treatment response in endometrial cancer and atypical hyperplasia: A prognostic study” by Chloe Barr et al. [1]. We applaud the authors’ diligent efforts in investigating a biomarker that could independently predict the response to conservative therapy. However, we wish to draw attention to certain noteworthy aspects upon a comprehensive evaluation.Firstly, it is noteworthy that all the women who participated in the study underwent a preliminary endometrial biopsy before the initiation of progestin. However, there is no mention of whether women with relative contraindications such as cervical stenosis, coagulopathy or obstructive cervical lesions were sampled if they were included in the study. It is essential to consider these factors as they can significantly affect the accuracy and reliability of the biopsy results. Furthermore, it is necessary to note that insufficient tissue sampling is a common complication of endometrial biopsy, with an average of 31% of tissues obtained requiring improvement [2]. Considering that this is typically more prevalent in postmenopausal women, and 61% of the participants were 50 years or older, it is crucial to standardize the volume of tissue obtained to ensure fair and precise results. As outlined in the study, the primary form of progestin therapy was levonorgestrel-releasing intrauterine system (LNG-IUS). Still, for women whose devices had been misplaced more than once, an alternative treatment of oral medroxyprogesterone acetate 500mg was administered twice daily. This raises a concern regarding whether these women were closely monitored for compliance with the prescribed treatment regimen. This is particularly important as non-compliance, particularly with extended oral therapies, is a common issue that, if present, could skew the study’s findings. The prognostic potential of pretreatment serum HE4 in predicting therapeutic response has been extensively researched; however, studies have also reported elevated serum HE4 levels in various other cancers, including ovarian, pancreatic, breast, lung, and stomach [3]. Therefore, it is crucial to exclude such patients thoroughly, as their inclusion could lead to inaccurate results by falsely accounting for the non-responder count.Moreover, serum HE4 levels are also known to be influenced by renal function and status, necessitating adjustment [4]. It is, therefore, essential to consider and standardize these factors when analyzing the serum HE4 levels to obtain reliable and valid results. Lastly, it should be noted that a CLEIA technique was employed for analysis, which has been reported to significantly overestimate serum HE4 as compared to EIA [5]. This may raise concerns regarding the validity of the reported findings, and hence, caution must be exercised when interpreting the results.The study focused on endometrial biopsy in women receiving progestin therapy, but potential complications such as insufficient tissue sampling and the inclusion of women with contraindications were not addressed. The study primarily used LNG-IUS but also administered oral medroxyprogesterone acetate, and compliance monitoring was not discussed. Serum HE4 levels were examined, but patients with other cancers or renal issues were not excluded, and the CLEIA technique used for analysis may have overestimated results. Therefore, caution is necessary when interpreting the findings of this study.

A fast ventricular tachycardia was apparently induced in a patient with a CRT-D device by the delivery of a pacemaker stimulus whose timing corresponded with the timing of the QRS complex in the far-field electrogram. Appropriate programming of the device might have prevented this complication.

A 78-year-old male patient was brought via the Primary percutaneous coronary intervention (PPCI) service to our hospital for urgent angiography. He had been discharged a week ago, after presenting with chest pain and undergoing Primary Percutaneous intervention (PCI) to the Left Circumflex artery, followed by Permanent pacemaker insertion for treatment of Sick Sinus Syndrome. CT Coronary angiography/CT Chest demonstrated a right ventricular lead perforation, alongside an occlusion in the mid-distal segment of the recently inserted stent in the circumflex artery. The diagnosis required careful interpretation of the pacing check, and a high clinical suspicion of lead perforation. The perforated lead was removed, and a new lead positioned in the Right ventricular septum the following day. There was also an attempt to open the occluded circumflex artery, but this was unsuccessful.

We present a 35-year-old male who sustained a right hip GSI. Delayed THA with a two-step sequential approach is feasible management in this situation in order to manage soft tissue and decrease infection rate. At one-year follow-up visit the pain was relieved and function improved significantly and had no complaints.

Background: Chronic rhinosinusitis (CRS) is a heterogeneous disease characterized by persistent sinonasal inflammation and sinus microbiome dysbiosis. Nasal polyps (NPs) is one of the main manifestations which cause diverse clinical symptoms of CRS. We conducted a bibliometric and visual analysis of CRS and NPs articles published between 2003 and 2022 to provide researchers with the current state of research and potential directions. Method: We used a systematic search strategy to search relevant articles in the databases of the Web of Science from 2003 to 2022. VOSviewers, Bibliometrix R package, and CiteSpace were used to perform the bibliometric analysis. Results: 3,907 publications, including 3,266 “articles” and 641 “reviews” were retrieved. The USA made the highest contributions to global research, followed by China; furthermore, Northwestern University, Capital Medical University, and Sun Yat-Sen University had the highest number of publications. A total of 12,894 authors participated in this research, with the most published author being Bachert C., followed by Schleimer Robert P. and Schlosser Rodney J.. And the authors with the most co-citations were Bachert C., Fokkens W.J., and Gevaert P. 428 journals had published the articles of this research. Moreover, the journal with the most publications was the International Forum Of Allergy & Rhinology and the Journal Of Allergy And Clinical Immunology received the most citations. “Covid-19”, “biologic”, and “type 2 inflammation” were the top current research hotspots. Conclusion: The United States and Northwestern University were the leading country and institution in researching CRS and NPs. And Bachert C. is the most influential expert. The International Forum Of Allergy & Rhinology published the most articles, and the Journal Of Allergy And Clinical Immunology got the highest number of citations. Moreover, “Covid-19”, “biologic”, and “type 2 inflammation” were the trending topics.

It is shown experimentally a new digital optical decoding scheme based on the transmission or polarized light at p polarization planes using a K-Nearest Neighbor (KNN) algorithm through a single-mode optical fibre at 633 nm. The optical power signal is sent at p polarization planes which constitute p classes required for signal bit recognition. Results show that it is possible to recognize 32 polarizations planes, 5 bits, using 4 features corresponding to the measurement of optical power at 4 different angles at the photodetector side with an average assertiveness of 99.1%.

The Fe2+ activated persulfate and H2O2 (Fe2+/KPS/H2O2) system achieved 94% removal of four mixed sulfonamides with 300 s treatment and possessed excellent complementarity and stability in a wide of pH (3 – 11) and temperature (5 – 65 ℃). The quenching and electron spin resonance spectrometer results confirmed that sulfate radicals and hydroxyl radicals coexisted in the coupled system and were responsible for eliminating sulfonamides under ambient conditions. Experimental determination and density functional theory calculations demonstrated that the reaction rate constants of sulfate radicals and hydroxyl radicals at possible reactive sites distinguished the difference in removal ratios of four sulfonamides. The removal ratio of sulfathiazole was higher than others because its calculated reaction rate constants of sulfate radicals and hydroxyl radicals were higher than those of sulfamerazine, sulfamethoxazole, and sulfamethazine. The finding provided a reference for investigating the removal mechanism of mixed organic pollutants when multiple free radicals coexist.

When acting as the shared energy storage and participating in electricity market services, the schedulable capacity that shared electric vehicle (shared EV) will provide to the grid needs in future time needs to be predicted accurately. This research first proposes a method to construct a schedulable capacity dataset for shared EVs based on publicly available shared vehicle rental service data. Secondly, a schedulable capacity evaluation model based on model-agnostic meta-learning, convolutional neural network, long short-term neural network and attention mechanism (MAML-CNN-LSTM-Attention) is proposed. Through the model, the aggregated schedulable capacity of shared EVs in different functional communities for the coming 60 minutes is predicted. Model uses MAML to fine-tune the meta-prediction network through multi-task training to quickly adapt to feature changes caused by different travel habits of different functional communities; CNN-LSTM is used to learn spatial features of schedulable capacity and efficiently extract high-dimensional temporal features from historical sequences; Attention mechanism is used to further improve model prediction accuracy. Simulations show that the model proposed in this paper outperforms other existing models and can reliably predict the schedulable capacity for different date types and functional areas, providing useful decision aids for shared EV operators to participate in market services.

The high penetration of renewable energy resources integrated via power electronic inverters in weak and low-inertia grids is introducing new challenges to power systems. Inverter-based resources (IBRs) do not have native inertia similar to fossil-fuel-based generators and can cause sustained oscillations and instability. Synchronous Condensers (SynCons) are considered as a reborn technology to provide a solution for system strengthening and inertia supporting in IBR-dominant power systems. Although SynCons are a well-known technology, additional assessment for analyzing the stability of a weak grid with a high penetration of renewable resources in the presence of SynCons is required. This paper proposes a quantitative index for the stability analysis of a system, which includes black-boxed IBRs and SynCon. The proposed index is stemmed from impedance-based stability analysis. In this paper, the impact of a SynCon on the proposed stability index is evaluated in a single-machine infinite-bus system, and its accuracy is also verified in an IEEE 39-bus system. Additionally, the findings are verified using time-domain simulation tests in PSCAD/EMTDC software.

Introduction: Nasolacrimal duct obstruction (NLDO) is the most common cause of childhood epiphora. Congenital NLDO is usually conservatively management in the first year of life, failing which surgical interventions such as syringing and probing (S&P), insertion of stents (intubation) or dacryocystorhinostomy (DCR) are offered in a stepwise manner. Methods: This is a retrospective study at a tertiary paediatric hospital. Nasolacrimal surgeries were retrieved from Hospital Episodes Statistics (HES) data for a 5-year period between May 2017 to April 2022. Retrospective case note review was undertaken looking into demographics, presentation, surgical interventions, and outcomes (resolved, partially resolved or persistent). Results: In our institution, NLDO surgeries are performed on a joint ophthalmology/ENT list. A total of 301 procedures were performed on 218 patients (293 eyes). Causes for epiphora were Congenital NLDO (n=193, 88.5%), Secondary NLDO (n=10, 4.6%), Dacryocystitis/Mucocele (n=8, 3.67%) among others. Median age at first procedure was 26 months (range 2-189). Median number of procedures for congenital NLDO is 1(range 1-5). Complete resolution of symptoms was achieved by syringing and probing in 133 cases (73%), intubation in 23 cases (78%) and DCR in 7 cases(58.3%). Patients with craniofacial syndromes required a statistically significant higher number of DCRs. Overall, epiphora was completed resolved in 81% cases, 6.3% partially resolved and is persistent in 12.7%. Conclusion: Multi-disciplinary approach to NLDO ensured efficient delivery of care by minimising number of procedures and hospital attendance. Congenital NLDO can be successfully treated in vast majority of cases with S&P and intubation. In cases with anatomical abnormalities, DCR should be considered early.

The influence of preloading on the residual fatigue life of nickel-based superalloys under elevated temperatures was investigated experimentally. A powder metallurgy nickel-based superalloy FGH96 was preloaded with different number of cycles, and the residual fatigue life was tested under subsequent high-amplitude loads. The test results show that the fatigue life of the material was increased when the preloading cycle number was within a specific range. At the same time, the fatigue life of virgin specimens under high-amplitude loads shows little scatter, which provides the possibility to consider the strengthening effect of low-amplitude loads. A novel damage accumulation model was proposed to incorporate the strengthening effect of low-amplitude loads into the life prediction framework. The proposed model provides better life prediction than some existing models. Finally, the proposed model was validated using experimental data for various materials under the low-high loading sequence.

Gametic isolation is thought to play an important role in the evolution of reproductive isolation in broadcast-spawning marine invertebrates. However, it remains uncertain whether gametic isolation typically develops early in the speciation process or accumulates after other reproductive barriers are already in place. It is also unknown whether gametic incompatibilities have effectively prevented introgression during later stages of divergence. Here, we use phylogenomic approaches to test whether the well-documented asymmetric gametic incompatibilities between strongylocentrotid urchins have been effective in preventing introgression. Despite a well-supported species tree, we found considerable phylogenetic discordance that cannot be explained by incomplete lineage sorting alone. There was strong support for introgression between at least four pairs of extant taxa: S. pallidus ↔ S. droebachiensis, S. intermedius ↔ S. pallidus, S. purpuratus ↔ S. fragilis, and M. franciscanus ↔ P. depressus. There was additional evidence for introgression on internal branches of the phylogeny. Although gametic incompatibilities may be important in species recognition and the maintenance of species boundaries in strongylocentrotid urchins, gametic isolation does not appear to have been an effective barrier to introgression. The continued divergence in the face of widespread introgression indicates that other reproductive isolating barriers likely exist and may have been more critical in establishing reproductive isolation early in speciation.

Human activities have altered the composition of species assemblages through the introduction of non-native species and the extinction of threatened species. However, it remains unclear whether non-native species can compensate for the loss of threatened species and thus maintain ecosystem functioning. Here we tested whether non-natives are functionally and/or phylogenetically similar (compensation hypothesis) or distinct (shift hypothesis) from native and threatened species on bird assemblages in 267 regions worldwide. We show that non-native species were more functionally distinct from threatened species than expected by chance but more phylogenetically related. Globally, this results in an increase in the functional richness of bird assemblages due to the introduction of new functional traits but a decrease in the phylogenetic richness due to the potential loss of phylogenetically unique threatened species. Furthermore, these patterns vary across continents, revealing the role of human history and footprint across the world and outlining priority areas where international bird conservation should focus. In the context of the biodiversity crisis and the increasing number of non-native species worldwide, the changes in the functional and phylogenetic structure of the bird communities might increase the vulnerability of ecosystems.

α-Glucosidase (AG) is a bifunctional enzyme, it has a capacity to synthesize 2-O-α-D-glucopyranosyl-L-ascorbic acid (AA-2G) from L-ascorbic acid (L-AA) and low-cost maltose under mild conditions, but it can also hydrolyze AA-2G, which leads to low synthesis efficiency of AA-2G. Main Methods and Major Results This study introduces a rational molecular design strategy to regulate enzymatic reactions based on inhibiting the formation of ground state of enzyme-substrate complex. Y215 was analyzed as the key amino acid site affecting the affinity of AG to AA-2G and L-AA. For the purpose of reducing the hydrolysis efficiency of AA-2G, the mutant Y215W was obtained by analyzing the molecular docking binding energy and hydrogen bond formation between AG and the substrates. Compared with the wild type, Isothermal Titration Calorimetry(ITC) results showed that the equilibrium dissociation constant (KD) of the mutant for AA-2G was doubled; the Michaelis constant (Km) for AA-2G was reduced by 1.15 times; and the yield of synthetic AA-2G was increased by 39%. Conclusions and Implications Our work also provides a new reference strategy for the molecular modification of multifunctional enzymes and other enzymes in cascade reactions system.

[Abstract] Objective: To develop a pneumonia risk prediction model for SARS-CoV-2 infected patients to reduce unnecessary chest CT scans; Materials and Methods: Retrospective analysis was performed on the clinical data of SARS-CoV-2-positive patients who visited outpatient and emergency clinics and underwent chest CT scans at the Mawangdui Branch of Hunan Provincial People’s Hospital from 20 December 2022 to 23 December 2022 and at the Tianxinge Branch of Hunan Provincial People’s Hospital from 1 January 2023 to 4 January 2023. A retrospective analysis of imaging and clinical data from 205 cases (training cohort) and 94 cases (validation cohort) of SARS-CoV-2-positive patients who visited outpatient and emergency clinics was conducted. The predictor variables were screened using the “univariate and then multivariate logistic regression” and “least absolute shrinkage and selection operator (LASSO)” approaches, and the predictive model was constructed using multifactorial logistic regression and represented as a nomogram. The diagnostic effectiveness of the pneumonia risk model was evaluated using receiver operating characteristic (ROC) curves; the Delong test and Integrated Discrimination Improvement Index (IDI) were used to compare the AUC of the pneumonia risk model with the AUCs for predictors incorporated in the model alone. The calibration of the pneumonia risk model was assessed using calibration curves; Decision curve analysis (DCA) was used to evaluate the clinical validity of the pneumonia risk model. In addition, a smoothed curve was fitted using a generalized additive model (GAM) to explore the relationship between the pneumonia grade and the model’s predicted probability of pneumonia; Results: “univariate and then multivariate logistic regression ” and Lasso regression together show that age, natural log-transformed value (InCRP), Monocytes percentage (%Mon) are valid predictors of pneumonia risk; the AUC of the pneumonia risk model was 0.7820 (95% CI: 0.7254-0.8439) in the training cohort and 0.8432 (95% CI: 0.7588-0.9151) in the validation cohort; at the cut-off value of 0.5, the sensitivity and specificity of the pneumonia risk model were 70.75%, 66.33% (training cohort), 76.09%, and 73.91% (validation cohort), the calibration curves showed that the pneumonia risk model has good calibration accuracy. The decision curve analysis showed that the pneumonia risk model has high clinical value in predicting the probability of pneumonia in SARS-CoV-2 infected patients. Conclusion: The pneumonia risk prediction model developed in this study can be used to predict the risk of pneumonia in SARS-CoV-2 infected patients diagnostically.