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ObjectiveTo establish, evaluate and preliminarily apply grey model (1,1) [GM(1,1)] models for predicting the prevalence of common carbapenem-resistant bacteria in China. MethodsWe extracted the national data of 2015 – 2021 on detection rate of carbapenem-resistant Escherichia coli (CR-E.coli), carbapenem-resistant Klebsiella pneumoniae (CRKP), carbapenem-resistant Pseudomonas aeruginosa (CRPA), and carbapenem-resistant Acinetobacter baumannii (CRAB) from the China Bacterial Drug Resistance Surveillance Network. Four GM(1,1) models were constructed using the data of 2015 – 2019. Posterior difference ratio C, small error probability P, and relative error and residual error were used to assess the accuracy of the established models in predicting prevalence of common carbapenem-resistant bacteria with the data of 2020 – 2021. The prevalence of CR-E.coli, CRKP, CRPA and CRAB for years from 2022 to 2025 in China were predicted using the four GM(1,1) models established. ResultsFor the four established GM(1,1) models, the values of posterior difference ratio C were less than 0.50, the small probability error P were close to 1.0, and the relative errors and residual errors were less than 10%, indicating good efficacies in predicting the prevalence of CR-E.coli, CRKP, CRPA and CRAB. Based on the four established GM(1,1) models, the predicted yearly prevalence of CR-E.coli, CRKP, CRPA, and CRAB in China are 1.668%, 12.208%, 16.663%, and 52.507% for 2022; 1.698%, 12.886%, 15.930%, and 1.549% for 2023; 1.729%, 13.601%, 15.229%, and 50.608% for 2024; and 1.761%, 14.355%, 14.559%, and 49.685% for 2025，respectively, with a upward trend for the prevalence of CR-E.coli and CRKP but a downward trend for the prevalence of CRPA and CRAB. ConclusionIn this study, four GM(1,1) models were established for predicting the prevalence of common carbapenem-resistant bacteria and the predictions based on the models could be used in developing strategies on the prevention and control the prevalence of CR-E.coli, CRKP, CRPA and CRAB in China.

Breeding for nitrogen use efficiency (NUE) is becoming more important as global uncertainty makes the production and application of nitrogen (N) fertilizers more expensive and environmentally unfriendly. Despite this, most cereal breeding programs still use yield-related components as proxies for NUE, likely due to the prohibitive cost and time of collecting and analyzing samples through traditional lab-based methods. Drone-based NUE phenotyping provides a viable and scalable alternative as it is quicker, non-destructive, and consistent. Here, we present a study that utilized financially accessible cost-effective drones mounted with red-green-blue (RGB) image sensors coupled with the open-source AirMeasurer platform and advanced statistical analysis to exclude low-NUE lines in multi-seasonal field experiments. The method helped us to identify high N agronomic use efficiency lines but was less effective with a high N recovery efficiency line. We found that the drone-powered approach was very effective at 180 kg N per hectare (N180, an optimized N-rate) as it completely removed low-NUE wheat lines in the trial, which would facilitate breeders to quickly reduce the number of lines taken through multi-year breeding programs. Hence, this encouraging and scalable approach demonstrates its ability to conduct NUE phenotyping in wheat. With continuous refinements in field experiments, this method would be employable as an openly accessible platform to identify NUE lines at different N-rates for breeding and resource use efficiency studies in wheat.

Wheat (Triticum aestivum) is one of the most important staple crops worldwide. To ensure its global supply, the timing and duration of its growth cycle needs to be closely monitored in the field so that necessary crop management activities can be arranged in a timely manner. Also, breeders and plant researchers need to evaluate growth stages (GSs) for tens of thousands of genotypes at the plot level, at different sites and across multiple seasons. These indicate the importance of providing a reliable and scalable toolkit to address the challenge so that the plot-level assessment of GS can be successfully conducted for different objectives in plant research. Here, we present a multimodal deep learning model called GSP-AI, capable of identifying key GSs and predicting the vegetative-to-reproductive transition (i.e., flowering days) in wheat based on drone-collected canopy images and multiseasonal climatic datasets. In the study, we first established an open Wheat Growth Stage Prediction (WGSP) dataset, consisting of 70,410 annotated images collected from 54 varieties cultivated in China, 109 in the United Kingdom, and 100 in the United States together with key climatic factors. Then, we built an effective learning architecture based on Res2Net and long short-term memory (LSTM) to learn canopy-level vision features and patterns of climatic changes between 2018 and 2021 growing seasons. Utilizing the model, we achieved an overall accuracy of 91.2% in identifying key GS and an average root mean square error (RMSE) of 5.6 d for forecasting the flowering days compared with manual scoring. We further tested and improved the GSP-AI model with high-resolution smartphone images collected in the 2021/2022 season in China, through which the accuracy of the model was enhanced to 93.4% for GS and RMSE reduced to 4.7 d for the flowering prediction. As a result, we believe that our work demonstrates a valuable advance to inform breeders and growers regarding the timing and duration of key plant growth and development phases at the plot level, facilitating them to conduct more effective crop selection and make agronomic decisions under complicated field conditions for wheat improvement.

Abstract Background The overuse of antibiotics in primary healthcare settings (PHSs) has caused a serious public health problem in China. The outbreak of the Coronavirus Disease-19 (COVID-19) pandemic brought about dramatic changes in the supply of and demand for medical services in PHSs, possibly resulting in unprecedented changes in antibiotic use. Objective This study aims to assess the immediate and long-term impacts of the COVID-19 pandemic on the changes in antibiotic consumption in PHSs. Method The data on antibiotic consumption were collected from selected township hospitals in Shandong, China from January 2019 to December 2021. Antibiotic consumption was quantified by using the defined daily doses (DDDs) and the WHO Access, Watch, Reserve category. A segmented regression model was established to analyze the immediate and long-term impacts of the COVID-19 pandemic on antibiotic use by using the interrupted time series analysis. Results The overall antibiotic consumption in all PHSs decreased by 32.04% and 16.69% in 2020 and 2021 respectively compared to the corresponding period in 2019. Over the entire study period, the use of penicillins (J01C) and cephalosporins (J01D) accounted for more than 50% of the total antibiotic consumption. The average annual consumption of Watch category antibiotics decreased by 42.02% and 33.47% in 2020 and 2021 respectively compared to that in 2019. According to the interrupted time series analysis, the total antibiotic consumption decreased significantly immediately after the COVID-19 pandemic outbreak (coef. = − 2.712, p = 0.045), but it then increased significantly over a long-term (coef. = 0.205, p = 0.005). Additionally, the consumption of Access category antibiotics increased significantly in PHSs in the long-term (coef. = 0.136, p = 0.018). However, the consumption of Watch category antibiotics declined sharply immediately after the pandemic (coef. = − 1.222, p

Rice (Oryza sativa) is an essential stable food for many rice consumption nations in the world and, thus, the importance to improve its yield production under global climate changes. To evaluate different rice varieties’ yield performance, key yield-related traits such as panicle number per unit area (PNpM2) are key indicators, which have attracted much attention by many plant research groups. Nevertheless, it is still challenging to conduct large-scale screening of rice panicles to quantify the PNpM2 trait due to complex field conditions, a large variation of rice cultivars, and their panicle morphological features. Here, we present Panicle-Cloud, an open and artificial intelligence (AI)-powered cloud computing platform that is capable of quantifying rice panicles from drone-collected imagery. To facilitate the development of AI-powered detection models, we first established an open diverse rice panicle detection dataset that was annotated by a group of rice specialists; then, we integrated several state-of-the-art deep learning models (including a preferred model called Panicle-AI) into the Panicle-Cloud platform, so that nonexpert users could select a pretrained model to detect rice panicles from their own aerial images. We trialed the AI models with images collected at different attitudes and growth stages, through which the right timing and preferred image resolutions for phenotyping rice panicles in the field were identified. Then, we applied the platform in a 2-season rice breeding trial to valid its biological relevance and classified yield production using the platform-derived PNpM2 trait from hundreds of rice varieties. Through correlation analysis between computational analysis and manual scoring, we found that the platform could quantify the PNpM2 trait reliably, based on which yield production was classified with high accuracy. Hence, we trust that our work demonstrates a valuable advance in phenotyping the PNpM2 trait in rice, which provides a useful toolkit to enable rice breeders to screen and select desired rice varieties under field conditions.

Inefficient nitrogen (N) utilization in agricultural production has led to many negative impacts such as excessive use of N fertilizers, redundant plant growth, greenhouse gases, long-lasting toxicity in ecosystem, and even effect on human health, indicating the importance to optimize N applications in cropping systems. Here, we present a multiseasonal study that focused on measuring phenotypic changes in wheat plants when they were responding to different N treatments under field conditions. Powered by drone-based aerial phenotyping and the AirMeasurer platform, we first quantified 6 N response-related traits as targets using plot-based morphological, spectral, and textural signals collected from 54 winter wheat varieties. Then, we developed dynamic phenotypic analysis using curve fitting to establish profile curves of the traits during the season, which enabled us to compute static phenotypes at key growth stages and dynamic phenotypes (i.e., phenotypic changes) during N response. After that, we combine 12 yield production and N-utilization indices manually measured to produce N efficiency comprehensive scores (NECS), based on which we classified the varieties into 4 N responsiveness (i.e., N-dependent yield increase) groups. The NECS ranking facilitated us to establish a tailored machine learning model for N responsiveness-related varietal classification just using N-response phenotypes with high accuracies. Finally, we employed the Wheat55K SNP Array to map single-nucleotide polymorphisms using N response-related static and dynamic phenotypes, helping us explore genetic components underlying N responsiveness in wheat. In summary, we believe that our work demonstrates valuable advances in N response-related plant research, which could have major implications for improving N sustainability in wheat breeding and production.

Sperm carries male genetic information, and flagella help move the sperm to reach oocytes. When the ultrastructure of the flagella is abnormal, the sperm is unable to reach the oocyte and achieve insemination. Multiple morphological abnormalities of sperm flagella (MMAF) is a relatively rare idiopathic condition that is mainly characterized by multiple defects in sperm flagella. In the last decade, with the development of high-throughput DNA sequencing approaches, many genes have been revealed to be related to MMAF. However, the differences in sperm phenotypes and reproductive outcomes in many cases are attributed to different pathogenic genes or different pathogenic mutations in the same gene. Here, we will review information about the various phenotypes resulting from different pathogenic genes, including sperm ultrastructure and encoding proteins with their location and functions as well as assisted reproductive technology (ART) outcomes. We will share our clinical detection and diagnosis experience to provide additional clinical views and broaden the understanding of this disease.

Objective The intestinal microbiota plays an important role in host physiology, metabolism, immunity, and behavior. And host genetics could influence the gut microbiota of hybrid animals. The three-way cross model is commonly utilized in commercial pig production; however, the use of this model to analyse the gut microbial composition is rarely reported. Methods Two three-way hybrid pigs were selected, with Saba pigs as the starting maternal pig: Duroc× (Berkshire×Saba) (DBS) pig, Berkshire×(Duroc×Saba) (BDS) pig. One hundred pigs of each model were reared from 35 days (d) to 210 d. The body weight or feed consumption of all pigs were recorded and their feed/gain (F/G) ratio was calculated. On day 210, 10 pigs from each three-way cross were selected for slaughter, and cecal chyme samples were collected for 16S rRNA gene sequencing. Results The final body weight (FBW) and average daily gain (ADG) of DBS pigs were significantly higher than those of BDS pigs (p0.05). At the genus level, the abundance of Prevotella, Roseburia, and Anaerovibrio in DBS pigs was significantly lower than in BDS pigs (p

Abstract Background Antibiotic resistance poses a significant threat to public health globally. Irrational utilization of antibiotics being one of the main reasons of antibiotic resistant. Children as a special group, there's more chance of getting infected. Although most of the infection is viral in etiology, antibiotics still are the most frequently prescribed medications for children. Therefore, high use of antibiotics among children raises concern about the appropriateness of antibiotic prescribing. This systematic review aims to measuring prevalence and risk factors for antibiotic utilization in children in China. Methods English and Chinese databases were searched to identify relevant studies evaluating the prevalence and risk factors for antibiotic utilization in Chinese children (0-18 years), which were published between 2010 and July 2020. A Meta-analysis of prevalence was performed using random effect model. The Agency for Healthcare Research and Quality (AHRQ) and modified Jadad score was used to assess risk of bias of studies. In addition, we explored the risk factors of antibiotic utilization in Chinese children using qualitative analysis. Results Of 10,075 studies identified, 98 eligible studies were included after excluded duplicated studies. A total of 79 studies reported prevalence and 42 studies reported risk factors for antibiotic utilization in children. The overall prevalence of antibiotic utilization among outpatients and inpatients were 63.8% (35 studies, 95% confidence interval (CI): 55.1-72.4%), and 81.3% (41 studies, 95% CI: 77.3-85.2%), respectively. In addition, the overall prevalence of caregiver’s self-medicating of antibiotics for children at home was 37.8% (4 studies, 95% CI: 7.9-67.6%). The high prevalence of antibiotics was associated with multiple factors, while lacking of skills and knowledge in both physicians and caregivers was the most recognized risk factor, caregivers put pressure on physicians to get antibiotics and self-medicating with antibiotics at home for children also were the main factors attributed to this issue. Conclusion The prevalence of antibiotic utilization in Chinese children is heavy both in hospitals and home. It is important for government to develop more effective strategies to improve the irrational use of antibiotic, especially in rural setting.

Objective: This study aimed to explore how clinical uncertainty influences antibiotic prescribing practices among township hospital physicians and village doctors in rural Shandong Province, China. Methods: Qualitative semi-structured interviews were conducted with 30 township hospital physicians and 6 village doctors from rural Shandong Province, China. A multi-stage random sampling method was used to identify respondents. Conceptual content analysis together with Colaizzi’s method were used to generate qualitative codes and identify themes. Results: Three final thematic categories emerged during the data analysis: (1) Incidence and treatment of Upper Respiratory Tract Infections (URTIs) in township hospitals and village clinics; (2) Antibiotic prescribing practices based on the clinical experience of clinicians; (3) Influence of clinical uncertainty on antibiotic prescribing. Respondents from both township hospitals and village clinics reported that URTIs were the most common reason for antibiotic prescriptions at their facilities and that clinical uncertainty appears to be an important driver for the overuse of antibiotics for URTIs. Clinical uncertainty was primarily due to: (1) Diagnostic uncertainty (establishing a relevant diagnosis is hindered by limited diagnostic resources and capacities, as well as limited willingness of patients to pay for investigations), and (2) Insufficient prognostic evidence. As a consequence of the clinical uncertainty caused by both diagnostic and prognostic uncertainty, respondents stated that antibiotics are frequently prescribed for URTIs to prevent both prolonged courses or recurrence of the disease, as well as clinical worsening, hospital admission, or complications. Conclusion: Our study suggests that clinical uncertainty is a key driver for the overuse and misuse of prescribing antibiotics for URTIs in both rural township hospitals and village clinics in Shandong province, China, and that interventions to reduce clinical uncertainty may help minimize the unnecessary use of antibiotics in these settings. Interventions that use clinical rules to identify patients at low risk of complications or hospitalization may be more feasible in the near-future than laboratory-based interventions aimed at reducing diagnostic uncertainty.

Objectives: The overuse and misuse of antibiotics has accelerated the rapid emergence of antibiotic resistance. The aim of the study was to review interventions conducted in China to optimize use of antibiotics in humans, animals, and the environment from a One Health perspective. Methods: The literature review for this study was limited to English and Chinese articles published from January 1985 to May 2021. Literature review searches were conducted using Web of Science, Scopus, PubMed and three biomedical databases from China (the Chinese Scientific Journals database, the Wanfang Database, and China National Knowledge Infrastructure). We used Arksey and O’Malley's step-wise methodological framework as the basis for our scoping review. Results: A total of 53 studies met our inclusion criteria, of which 51 (96%) were from human healthcare settings, one from environment health that pertained to rural ponds, and no studies were found that met our criteria on interventions used to improve antibiotic use in animals. For human health, the majority of the research was related to antibiotic intervention programs performed in public institutions, and only one policy assessment study included private institutions. Interventions were classified into four broad categories: 1) Knowledge interventions; 2) decision support; 3) financial incentives; and 4) organizational/management systems. Our findings indicated that combinations of multiple interventions were more effective in promoting the rational use of antibiotics in China. Conclusions: China has made major efforts on improving rational use of antibiotics in the past decades. Most policies or interventions, however, focused mainly on the human health aspect, less effort targeted toward the environment and animal health sectors. For further optimizing use of antibiotics, the cross-disciplinary and coordinated multi-faceted interventions guided by the One Health perspective should be developed and implemented. Meanwhile, the cross-departmental collaborative mechanism leading by the Chinese central government should be further strengthened to play a greater and more active role in fighting against antibiotic resistance wholly.

Abstract Background Irrational antibiotics use in clinical prescription, especially in primary health care (PHC) is accelerating the spread of antibiotics resistance (ABR) around the world. It may be greatly useful to improve the rational use of antibiotics by effectively intervening providers’ prescription behaviors in PHC. This study aimed to systematically review the interventions targeted to providers’ prescription behaviors in PHC and its’ effects on improving the rational use of antibiotics. Methods The literatures were searched in Ovid Medline, Web of Science, PubMed, Cochrane Library, and two Chinese databases with a time limit from January 1st, 1998 to December 1st, 2018. The articles included in our review were randomized control trial, controlled before-and-after studies and interrupted time series, and the main outcomes measured in these articles were providers’ prescription behaviors. The Cochrane Collaboration criteria were used to assess the risk of bias of the studies by two reviewers. Narrative analysis was performed to analyze the effect size of interventions. Results A total of 4422 studies were identified in this study and 17 of them were included in the review. Among 17 included studies, 13 studies were conducted in the Europe or in the United States, and the rest were conducted in low-income and-middle-income countries (LMICs). According to the Cochrane Collaboration criteria, 12 studies had high risk of bias and 5 studies had medium risk of bias. There was moderate-strength evidence that interventions targeted to improve the providers’ prescription behaviors in PHC decreased the antibiotics prescribing and improved the rational use of antibiotics. Conclusions Interventions targeted PHC providers’ prescription behaviours could be an effective way to decrease the use of antibiotics in PHC and to promote the rational use of antibiotics. However, we cannot compare the effects between different interventions because of heterogeneity of interventions and outcome measures.

Objectives: Existing findings regarding the relationship between comorbidities and the severity of coronavirus disease 2019 (COVID-19) are inconsistent and insufficient. The aim of this study was to evaluate the association between different comorbidities and the severity of COVID-19. Methods: The PubMed, Embase, and Cochrane Library databases were searched to identify studies reporting the rates of comorbidities in COVID-19 patients with severe/fatal outcomes. Subgroup analyses were conducted according to disease severity and the country of residence. Odds ratios (OR) with 95% confidence intervals (CI) were pooled using random-effects models. Results: A total of 34 eligible studies were identified. In patients with severe/fatal COVID-19, the most prevalent chronic comorbidities were obesity (42%, 95% CI 34–49%) and hypertension (40%, 95% CI 35–45%), followed by diabetes (17%, 95% CI 15–20%), cardiovascular disease (13%, 95% CI 11–15%), respiratory disease (8%, 95% CI 6–10%), cerebrovascular disease (6%, 95% CI 4–8%), malignancy (4%, 95% CI 3–6%), kidney disease (3%, 95% CI 2–4%), and liver disease (2%, 95% CI 1–3%). In order of the prediction, the pooled ORs of the comorbidities in patients with severe or fatal COVID-19 when compared to patients with non-severe/fatal COVID-19 were as follows: chronic respiratory disease, OR 3.56 (95% CI 2.87–4.41); hypertension, OR 3.17 (95% CI 2.46–4.08); cardiovascular disease, OR 3.13 (95% CI 2.65–3.70); kidney disease, OR 3.02 (95% CI 2.23–4.08); cerebrovascular disease, OR 2.74 (95% CI 1.59–4.74); malignancy, OR 2.73 (95% CI 1.73–4.21); diabetes, OR 2.63 (95% CI 2.08–3.33); and obesity, OR 1.72 (95% CI 1.04–2.85). No correlation was observed between liver disease and COVID-19 aggravation (OR 1.54, 95% CI 0.95–2.49). Conclusions: Chronic comorbidities, including obesity, hypertension, diabetes, cardiovascular disease, cerebrovascular disease, respiratory disease, kidney disease, and malignancy are clinical risk factors for a severe or fatal outcome associated with COVID-19, with obesity being the most prevalent and respiratory disease being the most strongly predictive. Knowledge of these risk factors could help clinicians better identify and manage the high-risk populations.

Neutrophils are crucial in the process of gout flare and remission. The signal transduction pathway of chemokine plays a vital role in the chemotaxis and activation of neutrophils. CXCR2 gene knocked out can avoid the acute neutrophilic inflammation stimulated by monosodium urate (MSU) crystals in mice. To investigate the relationship among CXCR1 rs2234671, CXCR2 rs1126579, and rs2230054 polymorphisms with gout arthritis flare in the Chinese Han male population, a case-control study was carried out in 412 gout patients and 508 gout-free individuals. TaqMan probes fluorescence real-time polymerase chain reaction (PCR) was used to genotype CXCR1 rs2234671, CXCR2 rs1126579, and rs2230054 SNPs. There was a clear link between CXCR2 rs2230054 T included genotypic and T allelic frequencies and gout cases (c2 = 9.286, p = 0.002 by genotype, c2 = 8.639, p = 0.003 by allele), while no significant differences were observed between the gouty arthritis group and the control group in CXCR1 rs2234671 and CXCR2 rs1126579 genotypic and allelic frequencies. Multivariate logistic regression analysis showed that the T genotype included in rs2230054 can decrease the risk of gouty arthritis (adjusted OR = 0.47; 95% CI: 0.31-0.74) compared with the CC genotype. Our study might suggest that rs2230054 in CXCR2 is associated with susceptibility to gout in Chinese males.

ObjectiveTo explore the influencing factors and cumulative risk of lymph node metastasis (LNM) in papillary thyroid microcarcinoma (PTMC) patients.Methods607 patients confirmed PTMC pathologically after thyroidectomy were enrolled in this retrospective study. The rate of LNM was calculated. Different clinicopathological characteristics were compared in PTMC patients with and without LNM and in different subgroups of LNM, respectively. Correlation between clinicopathological characteristics and LNM was analyzed and the cumulative risk of LNM according to different clinicopathological characteristics was calculated.Results(1) There were 228 cases (37.56%) of PTMC combined with LNM. Compared with the non-lymph node metastasis group, the proportion of age 0.5cm, extracapsular invasion, HT and intermediate-to-high risk stratification for recurrence of the LNM group was significantly increased (all p0.5cm,HT were independently positively correlated with LNM (all p 5 and the positive rate of LN were both higher in male group than that in the female group. The proportion of multiple foci and HT in LLNM group was higher than that in CLNM group (all P0.5cm and HT were independent risk factors of LNM; HT was an independent risk factor for LNM in female

Sea cucumber is a kind of food. Holothurian glycosaminoglycan (hGAG) is extracted from the body wall of the sea cucumber. Administration of hGAG and cisplatin (DDP) together to treat lung cancer was investigated. Lung adenocarcinoma A549 cells were cultured and divided into 4 groups: control group, hGAG 100 µg/mL group, DDP 3 µg/mL group, and hGAG 100 µg/mL + DDP 3 µg/mL group. Cell inhibition and apoptosis was evaluated by CCK8 and Hoechst33258 staining. Cell cycle was tested by Annexin V-FITC/PI (propidium iodide) double-staining and flow cytometry. The expression of mRNA and protein of Bcl-2, Bax, caspase-3, and survivin were detected by reverse transcriptase-polymerase chain reaction and Western blot, respectively. The results showed that hGAG combined with DDP enhanced the inhibitory effect of DDP on A549 lung cells through apoptosis pathway. The mechanism of apoptosis may be related to the reduction of Bcl-2 and survivin, as well as the ascension of Bax and caspase-3. hGAG could promote A549 cell cycle arrest in G1 and G2 phase and improve the DDP chemotherapy effects on A549 cells.

Bone grafts: improving chances of graft acceptance Repairs to serious bone injuries may be improved by blocking a signaling pathway that causes newly forming membranes to fail. Masquelet’s technique involves placing acrylic spacers in areas of bone damage, inducing the formation of vascularised membranes which encourage the body to accept bone grafts. However, sometimes Masquelet’s membranes do not form correctly, leading to weaknesses in bone repairs and potential graft rejection. In experiments on rats, Qian Tang from Wenzhou Medical University, China, and coworkers found that a particular signaling pathway, D114/Notch1, was upregulated around 6 weeks post-operation, reducing blood vessel density and limiting new vessel growth, weakening the membranes. The team inhibited this pathway using an existing therapy that prevents blood clots. This treatment improved bone repairs by promoting the formation and function of blood vessels in membranes.

High-resolution digital elevation models (DEMs) of the lunar surface are crucial for lunar exploration missions and scientific research. The emergence of advanced technologies, such as Shape-from-Shading (SfS), has enabled the generation of pixel-wise high-resolution DEMs from monocular images of the lunar surface. However, SfS encounters significant challenges in locations with limited illumination, such as the lunar south pole, where the surface is largely covered by shadows. Therefore, this paper presents a novel shadow-constrained SfS approach for pixel-wise 3D surface reconstruction at the lunar south pole. The proposed approach uses multiple high-resolution images captured under different illumination conditions and an existing low-resolution DEM as the inputs and generates a high-resolution DEM with the same resolution as that of the input image through hierarchical SfS processing incorporating shadow constraints. Experiments were conducted using actual images collected by the Lunar Reconnaissance Orbiter (LRO) Narrow Angle Camera (NAC) at the lunar south pole. Comparisons with respect to photogrammetric DEMs generated from stereo NAC images show that the DEMs generated using the proposed approach exhibit the smallest root mean square error (RMSE). Moreover, shaded relief images rendered from the DEMs generated using the proposed approach demonstrate the highest similarity to the actual NAC images. Detailed profile comparisons further validate the effectiveness of the shadow constraint in optimizing 3D reconstruction in regions in proximity to shadows and within shadowed regions. The proposed shadow-constrained SfS approach can be used to generate high-resolution DEMs to support future missions to explore the lunar south pole, with applications including landing site evaluation and route planning for lunar probes or astronauts.