Risk assessment of imported malaria in China: a machine learning perspective.

BACKGROUND: Following China's official designation as malaria-free country by WHO, the imported malaria has emerged as a significant determinant impacting the malaria reestablishment within China. The objective of this study is to explore the application prospects of machine learning algorithms in imported malaria risk assessment of China. METHODS: The data of imported malaria cases in China from 2011 to 2019 was provided by China CDC; historical epidemic data of malaria endemic country was obtained from World Malaria Report, and the other data used in this study are open access data. All the data processing and model construction based on R, and map visualization used ArcGIS software. RESULTS: A total of 27,088 malaria cases imported into China from 85 countries between 2011 and 2019. After data preprocessing and classification, clean dataset has 765 rows (85 * 9) and 11 cols. Six machine learning models was constructed based on the training set, and Random Forest model demonstrated the best performance in model evaluation. According to RF, the highest feature importance were the number of malaria deaths and Indigenous malaria cases. The RF model demonstrated high accuracy in forecasting risk for the year 2019, achieving commendable accuracy rate of 95.3%. This result aligns well with the observed outcomes, indicating the model's reliability in predicting risk levels. CONCLUSIONS: Machine learning algorithms have reliable application prospects in risk assessment of imported malaria in China. This study provides a new methodological reference for the risk assessment and control strategies adjusting of imported malaria in China.

IL-17 in intervertebral disc degeneration: Mechanistic insights and therapeutic implications.

Intervertebral disc degeneration (IDD) serves as an independent risk factor for lower back pain and is closely associated with spinal musculoskeletal disorders, including lumbar disc herniation, radiculopathy, and myelopathy. Interleukin-17 (IL-17), also named IL-17A, is a critical signature cytokine of T-helper 17 cells. Upon binding to the IL-17 receptor A/C heterodimeric complex, IL-17 can trigger multiple signal transduction pathways to stimulate gene transcription and increase messenger RNA stability. IL-17 expression is significantly increased in degenerative disc tissue and shows a positive correlation with disease severity. IL-17 has been shown to accelerate the development of IDD by promoting extracellular matrix degradation, enhancing inflammatory response, inducing neoangiogenesis, and inhibiting nucleus pulposus cell autophagy and proliferation. Targeting IL-17 represents a novel and promising approach for the therapeutic intervention of IDD. In this review, we summarized the recent progression about the role of IL-17 in IDD and highlighted its therapeutic implications.

In Vitro and In Vivo Antibacterial Activities of a Novel Quinolone Compound, OPS-2071, against Clostridioides difficile.

OPS-2071 is a novel quinolone antibacterial agent characterized by low oral absorption that reduces the risk of adverse events typical of fluoroquinolone class antibiotics. The in vitro and in vivo antibacterial activities of OPS-2071 against Clostridioides difficile were evaluated in comparison to vancomycin and fidaxomicin. OPS-2071 exhibited potent antibacterial activity against 54 clinically isolated C. difficile strains with a MIC of 0.125 µg/ml (MIC50) and 0.5 µg/ml (MIC90), making it more active than vancomycin on a concentration basis (MIC50, 2 µg/ml; MIC90, 4 µg/ml) and comparable to fidaxomicin (MIC50, 0.063 µg/ml; MIC90, 8 µg/ml). OPS-2071 showed equally potent antibacterial activity against both hypervirulent and nonhypervirulent strains, while a significant difference in susceptibility to fidaxomicin was observed. Spontaneous resistance to OPS-2071 and vancomycin was not observed; however, resistance to fidaxomicin was observed at 4× MIC. The mutant prevention concentration of OPS-2071 was 16-fold lower than those of fidaxomicin and vancomycin, and the postantibiotic effect of OPS-2071 was longer than those of fidaxomicin and vancomycin. Also, OPS-2071 showed low systemic exposure, with OPS-2071 having 2.9% oral bioavailability at 1 mg/kg in rats. Furthermore, OPS-2071 showed significant in vivo efficacy at 0.0313 mg/kg/day (50% effective doses), 39.0-fold and 52.1-fold lower than those of vancomycin and fidaxomicin, respectively, in a hamster model of C. difficile infection. OPS-2071 has the potential to become a new therapeutic option for treating C. difficile infection.

Molecular surveillance of pfcrt, pfmdr1 and pfk13-propeller mutations in Plasmodium falciparum isolates imported from Africa to China.

BACKGROUND: The emergence and spread of multidrug resistance poses a significant risk to malaria control and eradication goals in the world. There has been no indigenous malaria cases reported in China since 2017, and China is approaching national malaria elimination. Therefore, anti-malarial drug resistance surveillance and tracking the emergence and spread of imported drug-resistant malaria cases will be necessary in a post-elimination phase in China. METHODS: Dried blood spots were obtained from Plasmodium falciparum-infected cases returned from Africa to China between 2012 and 2015, prior to anti-malarial drug treatment. Whole DNA were extracted and known polymorphisms relating to drug resistance of pfcrt, pfmdr1 gene, and the propeller domain of pfk13 were evaluated by nested PCR and sequencing. The haplotypes and prevalence of these three genes were evaluated separately. Chi-squared test and Fisher's exact test were used to evaluate differences among the different sub-regions of Africa. A P value < 0.05 was used to evaluate differences with statistical significance. The maps were created using ArcGIS. RESULTS: A total of 731 P. falciparum isolates were sequenced at the pfcrt locus. The wild type CVMNK was the most prevalent haplotype with prevalence of 62.8% and 29.8% of the isolates showed the triple mutant haplotype CVIET. A total of 434 P. falciparum isolates were successfully sequenced and pfmdr1 allelic variants were observed in only codons 86, 184 and 1246. Twelve haplotypes were identified and the prevalence of the wild type pfmdr1 NYD was 44.1%. The single mutant pfmdr1 in codons 86 and 184 was predominant but the haplotype NYY with single mutation in codon 1246 was not observed. The double mutant haplotype YFD was common in Africa. About 1,357 isolates were successfully sequenced of pfk13-propeller domain, the wild type was found in 1,308 samples (96.4%) whereby 49 samples (3.6%) had mutation in pfk13. Of 49 samples with pfk13 mutations, 22 non-synonymous and 4 synonymous polymorphic sites were confirmed. The A578S was the most common mutation in pfk13-propeller domain and three mutations associated with artemisinin resistance (M476I, R539T, P553L) were identified in three isolates. CONCLUSION: This study provides evidence that could give insight into potential issues with anti-malarial drug resistance to inform national drug policy in China in order to treat imported cases.

Percutaneous spinal endoscopy with unilateral interlaminar approach to perform bilateral decompression for central lumbar spinal stenosis: radiographic and clinical assessment.

BACKGROUND: Recently, a percutaneous spinal endoscopy unilateral posterior interlaminar approach to perform bilateral decompression has been proposed for use in treatment of lumbar spinal stenosis, As a development and supplement to traditional surgery, its advantages regarding therapeutic effects and prognosis, such as minor soft tissue damage, little intraoperative blood loss, and a quick return to daily life. However, there are few analyses of this surgery with a follow-up of more than 1 year,we conducted this study in order to quantitatively investigate radiographic and clinical efficacies of this surgery for central lumbar spinal stenosis. MATERIALS AND METHODS: Forty-six patients with central lumbar spinal stenosis were enrolled from January 2017 to July 2018. The visual analog scale (VAS) for back pain and leg pain, Oswestry disability index (ODI), modified MacNab criteria were used to evaluate clinical efficiency at preoperative and postoperative time points. The intervertebral height index (IHI), cross-sectional area of the spinal canal (CSAC), calibrated disc signal (CDS) and spinal stability were examined to assess radiographic decompression efficiency via magnetic resonance imaging and X-ray at preoperative and postoperative time points. RESULTS: The VAS score for lower back pain and leg pain improved from 7.50 ± 0.78 to 1.70 ± 0.66 and from 7.30 ± 0.79 to 1.74 ± 0.68, respectively, and the ODI improved from 72.35 ± 8.15 to 16.15 ± 4.51. In terms of modified MacNab criteria, 91.3% of the patients achieved good or excellent outcomes. Furthermore, significant changes after surgery were observed for the percentage of CSAC, increasing from 125.3 ± 53.9 to 201.4 ± 78 mm2; however, no significant differences were observed for the remaining measurement indicators. CONCLUSIONS: The clinical and radiographic efficacies of this surgery for central lumbar spinal stenosis were good in short-term follow-up, and this surgery did not cause meaningful changes in IHI, CDS, and spine stability in short-term follow-up. The effect of long-term follow-up needs further investigation.

Research progress on the biological modifications of implant materials in 3D printed intervertebral fusion cages.

Anterior spine decompression and reconstruction with bone grafts and fusion is a routine spinal surgery. The intervertebral fusion cage can maintain intervertebral height and provide a bone graft window. Titanium fusion cages are the most widely used metal material in spinal clinical applications. However, there is a certain incidence of complications in clinical follow-ups, such as pseudoarticulation formation and implant displacement due to nonfusion of bone grafts in the cage. With the deepening research on metal materials, the properties of these materials have been developed from being biologically inert to having biological activity and biological functionalization, promoting adhesion, cell differentiation, and bone fusion. In addition, 3D printing, thin-film, active biological material, and 4D bioprinting technology are also being used in the biofunctionalization and intelligent advanced manufacturing processes of implant devices in the spine. This review focuses on the biofunctionalization of implant materials in 3D printed intervertebral fusion cages. The surface modifications of implant materials in metal endoscopy, material biocompatibility, and bioactive functionalizationare summarized. Furthermore, the prospects and challenges of the biofunctionalization of implant materials in spinal surgery are discussed. Fig.a.b.c.d.e.f.g As a pre-selected image for the cover, I really look forward to being selected. Special thanks to you for your comments.

Analysis of initial laboratory diagnosis of malaria and its accuracy compared with re-testing from 2013 to 2018 in Yunnan Province, China.

BACKGROUND: According to China's Malaria Eradication Action Plan, malaria cases diagnosed and reported by health authorities at the county level must be further re-confirmed by provincial laboratories. The Yunnan Province Malaria Diagnostic Reference Laboratory (YPMDRL) began the synchronous implementation of microscopic examinations and nested polymerase chain reaction (nested-PCR) testing to re-test the malaria cases initially diagnosed by county-level laboratories and to evaluate the consistency of Plasmodium species identified between by YPMDRL and by the county-level laboratories from 2013 to 2018 in Yunnan Province. METHODS: Data on malaria initial diagnosis completed by county-level laboratories in Yunnan Province were collected weekly from the "China Disease Prevention and Control Information System" from 2013 to 2018. The YPMDRL performed Plasmodium microscopic examination and 18S rRNA gene nested-PCR testing on every malaria case managed by the China Disease Prevention and Control Information System. The re-testing detection results were fed back to the initial diagnosis and reporting unit for revision of malaria case types. RESULTS: A total of 2,869 malaria cases were diagnosed and reported by county-level laboratories in Yunnan Province from 2013 to 2018. The re-testing rate was 95.6% (2,742/2,869), and the re-testing rate increased from 2013 to 2018. Among the re-tested 2,742 cases, 96.7% (2651/2742), 2.2% (59/2742), and 1.1% (32/2742) were doubly examined by microscopy and by nested-PCR, only by microscopy, and only by nested-PCR, respectively. The total Plasmodium species accuracy rate at county-level laboratories was 92.6% (2,543/2,742) reference to the diagnosis by YPMDRL. Among the inconsistent 199 cases, they were identified as including 103 negative cases, 45 falciparum malaria cases, 30 vivax malaria cases, 11 ovale malaria cases, and 10 malariae malaria cases by YPMDRL. From 2013 to 2018, the revised and registered malaria cases by the China Disease Prevention and Control Information System in Yunnan Province was 2,747 cases, including 2,305 vivax malaria cases, 421 falciparum malaria cases, 11 ovale malaria cases, and 10 malariae malaria cases. CONCLUSIONS: The double re-testing strategy by microscopy and by gene testing increases the accuracy of diagnoses malaria in Yunnan Province, and gene testing can reliably differentiate Plasmodium species. The re-testing results provided by YPMDRL are the authoritative basis for revising malaria kind in Yunnan Province.

[Analysis of schistosomiasis prevalence in endemic areas using retrospective space-time permutation scan statistics].

Objective: To analyze the characteristics of schistosomasis prevalence by using the spatial epidemiological method, and test the application of retrospective space-time permutation scan statistics in determining mountainous and lake-type endemic areas of schistosomiasis. Methods: The data of schistosomasis in humans, cattle and snails in Jiangxi Province during 2009-2014 and in Yunnan Province during 2004-2013 were collected and analyzed. The temporal and spatial distribution of schistosomiasis endemic areas in the two provinces was analyzed with retrospective space-time permutation scan statistics. Results: The prevalence of schistosomiasis in residents and Oncomelania snails showed a trend of decline in Jiangxi, from 0.21% and 0.03% in 2009 to 0.01% and zero in 2014. A similar trend was found in cattle, from 1.25% in 2012 to 0.12% in 2014. The average annual percentage change (APC) in residents was-47.36%(P < 0.05). The space-time permutation clustering analysis revealed a temporal and spatial clustering of schistosomiasis prevalence from 2009 to 2014 in residents, cattle, and snails, with 3,2 and 1 clustering areas, respectively, all distributed in Poyang Lake Region. A similar declining trend of schistosomiasis prevalence was found in residents, snails and cattle in Yunnan during 2004-2013, from 2.49%,0.70% and 3.76% in 2004 to no infection in residents and snails and 0.02% in cattle in 2013. The APC in residents was-49.17%(P < 0.05). There was a temporal and spatial clustering of schistosomiasis prevalence during 2004-2013 in residents, cattle, and snails, with 2,2 and 6 clustering areas, respectively. Conclusion: A declining trend of schistosomiasis prevalence is shown in lake-type endemic areas in Jiangxi during 2009-2014 and in mountainous endemic areas in Yunnan during 2004-2013. The retrospective space-time permutation scan statistics reveal a clustering of schistosomiasis in humans, cattle, and snails, suggesting its applicability in analyzing the temporal and spatial distribution of schistosomiasis.

Precision Prediction for Dengue Fever in Singapore: A Machine Learning Approach Incorporating Meteorological Data.

OBJECTIVE: This study aimed to improve dengue fever predictions in Singapore using a machine learning model that incorporates meteorological data, addressing the current methodological limitations by examining the intricate relationships between weather changes and dengue transmission. METHOD: Using weekly dengue case and meteorological data from 2012 to 2022, the data was preprocessed and analyzed using various machine learning algorithms, including General Linear Model (GLM), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Decision Tree (DT), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms. Performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2) were employed. RESULTS: From 2012 to 2022, there was a total of 164,333 cases of dengue fever. Singapore witnessed a fluctuating number of dengue cases, peaking notably in 2020 and revealing a strong seasonality between March and July. An analysis of meteorological data points highlighted connections between certain climate variables and dengue fever outbreaks. The correlation analyses suggested significant associations between dengue cases and specific weather factors such as solar radiation, solar energy, and UV index. For disease predictions, the XGBoost model showed the best performance with an MAE = 89.12, RMSE = 156.07, and R2 = 0.83, identifying time as the primary factor, while 19 key predictors showed non-linear associations with dengue transmission. This underscores the significant role of environmental conditions, including cloud cover and rainfall, in dengue propagation. CONCLUSION: In the last decade, meteorological factors have significantly influenced dengue transmission in Singapore. This research, using the XGBoost model, highlights the key predictors like time and cloud cover in understanding dengue's complex dynamics. By employing advanced algorithms, our study offers insights into dengue predictive models and the importance of careful model selection. These results can inform public health strategies, aiming to improve dengue control in Singapore and comparable regions.

Feasibility of ultrasound radiomics based models for classification of liver fibrosis due to Schistosoma japonicum infection.

BACKGROUND: Schistosomiasis japonica represents a significant public health concern in South Asia. There is an urgent need to optimize existing schistosomiasis diagnostic techniques. This study aims to develop models for the different stages of liver fibrosis caused by Schistosoma infection utilizing ultrasound radiomics and machine learning techniques. METHODS: From 2018 to 2022, we retrospectively collected data on 1,531 patients and 5,671 B-mode ultrasound images from the Second People's Hospital of Duchang City, Jiangxi Province, China. The datasets were screened based on inclusion and exclusion criteria suitable for radiomics models. Liver fibrosis due to Schistosoma infection (LFSI) was categorized into four stages: grade 0, grade 1, grade 2, and grade 3. The data were divided into six binary classification problems, such as group 1 (grade 0 vs. grade 1) and group 2 (grade 0 vs. grade 2). Key radiomic features were extracted using Pyradiomics, the Mann-Whitney U test, and the Least Absolute Shrinkage and Selection Operator (LASSO). Machine learning models were constructed using Support Vector Machine (SVM), and the contribution of different features in the model was described by applying Shapley Additive Explanations (SHAP). RESULTS: This study ultimately included 1,388 patients and their corresponding images. A total of 851 radiomics features were extracted for each binary classification problems. Following feature selection, 18 to 76 features were retained from each groups. The area under the receiver operating characteristic curve (AUC) for the validation cohorts was 0.834 (95% CI: 0.779-0.885) for the LFSI grade 0 vs. LFSI grade 1, 0.771 (95% CI: 0.713-0.835) for LFSI grade 1 vs. LFSI grade 2, and 0.830 (95% CI: 0.762-0.885) for LFSI grade 2 vs. LFSI grade 3. CONCLUSION: Machine learning models based on ultrasound radiomics are feasible for classifying different stages of liver fibrosis caused by Schistosoma infection.

Analysis of Epidemiological and Issues Encountered in Case Reports on Echinococcosis - China, 2022.

What is already known about this topic?: Echinococcosis is classified as a Class C infectious disease in China. It is endemic in 370 counties located in the agricultural and pastoral regions of western China. What is added by this report?: This report provides a comprehensive overview of the cases of echinococcosis reported in China in 2022. Following a thorough evaluation conducted by provincial CDCs, it was identified that 105 new cases were not reported through the National Notifiable Disease Reporting System. Furthermore, there were 1,051 cases that were reported among patients who had been previously diagnosed with echinococcosis. What are the implications for public health practice?: The reported cases of echinococcosis in non-endemic counties of provincial-level administrative divisions where the disease is endemic need to be given more attention, as there is a potential risk of it spreading within the non-endemic areas. Inadequate reporting practices by clinical medical institutions are hindering the subsequent investigations carried out by CDCs. It is important to implement enhanced health promotion efforts that focus on high-risk populations to address unhealthy lifestyles.

Identification of habitat suitability for the dominant zoonotic tick species Haemaphysalis flava on Chongming Island, China.

Haemaphysalis ticks are pathogenic vectors that threaten human and animal health and were identified in Chongming, the third largest island in China. To understand the distribution of these ticks and determine their potential invasion risk, this study aimed to identify the habitat suitability of the dominant tick H. flava based on natural environmental factors. Geographic information system (GIS) images were combined with sample points from tick investigations to map the spatial distribution of H. flava. Data on 19 bioclimatic variables, environmental variables, and satellite-based landscapes of Chongming Island were retrieved to create a landcover map related to natural environmental determinants of H. flava. These data included 38 sites associated with the vectors to construct species distribution models with MaxEnt, a model based on the maximum entropy principle, and to predict habitat suitability for H. flava on Chongming Island in 2050 and 2070 under different climate scenarios. The model performed well in predicting the H. flava distribution, with a training area under the curve of 0.84 and a test area under the curve of 0.73. A habitat suitability map of the whole study area was created for H. flava. The resulting map and natural environment analysis highlighted the importance of the normalized difference vegetation index and precipitation in the driest month for the bioecology of H. flava, with 141.61 km2 (11.77%), 282.94 km2 (23.35%), and 405.30 km2 (33.69%) of highly, moderately, and poorly suitable habitats, respectively. The distribution decreased by 135.55 km2 and 138.82 km2 in 2050 and 2070, respectively, under the shared socioeconomic pathway (SSP) 1.2.6 climate change scenario. However, under SSP 5.8.5, the total area will decrease by 128.5 km2 in 2050 and increase by 151.64 km2 in 2070. From a One Health perspective, this study provides good knowledge that will guide tick control efforts to prevent the spread of Haemaphysalis ticks or transmission risk of Haemaphysalis-borne infections at the human-animal-environment interface on the island.

Global One Health index for zoonoses: A performance assessment in 160 countries and territories.

The One Health (OH) approach is used to control/prevent zoonotic events. However, there is a lack of tools for systematically assessing OH practices. Here, we applied the Global OH Index (GOHI) to evaluate the global OH performance for zoonoses (GOHI-Zoonoses). The fuzzy analytic hierarchy process algorithm and fuzzy comparison matrix were used to calculate the weights and scores of five key indicators, 16 subindicators, and 31 datasets for 160 countries and territories worldwide. The distribution of GOHI-Zoonoses scores varies significantly across countries and regions, reflecting the strengths and weaknesses in controlling or responding to zoonotic threats. Correlation analyses revealed that the GOHI-Zoonoses score was associated with economic, sociodemographic, environmental, climatic, and zoological factors. Additionally, the Human Development Index had a positive effect on the score. This study provides an evidence-based reference and guidance for global, regional, and country-level efforts to optimize the health of people, animals, and the environment.

How far has the globe gone in achieving One Health? Current evidence and policy implications based on global One Health index.

Background: In the 21st century, as globalization accelerates and global public health crises occur, the One Health approach, guided by the holistic thinking of human-animal-environment and emphasizing interdisciplinary collaboration to address global health issues, has been strongly advocated by the international community. An immediate requirement exists for the creation of an assessment tool to foster One Health initiatives on both global and national scales. Methods: Built upon extensive expert consultations and dialogues, this follow-up study enhances the 2022 global One Health index (GOHI) indicator system. The GOHI framework is enriched by covering three indices, e.g. external drivers index (EDI), intrinsic drivers index (IDI), and core drivers index (CDI). The comprehensive indicator system incorporates 13 key indicators, 50 indicators, and 170 sub I-indicators, utilizing a fuzzy analytic hierarchy process to ascertain the weight for each indicator. Weighted and summed, the EDI, IDI, and CDI scores contribute to the computation of the overall GOHI 2022 score. By comparing the ranking and the overall scores among the seven regions and across 160 countries/territories, we have not only derived an overall profile of the GOHI 2022 scores, but also assessed the GOHI framework. We also compared rankings of indicators and sub I-indicators to provide greater clarity on the strengths and weaknesses of each region within the One Health domains. Results: The GOHI 2022 performance reveals significant disparities between countries/territories ranged from 39.03 to 70.61. The global average score of the GOHI 2022 is 54.82. The average score for EDI, IDI, and CDI are 46.57, 58.01, and 57.25, respectively. In terms of global rankings, countries from North America, Europe and Central Asia, East Asia and Pacific present higher scores. In terms of One Health domains of CDI, the lowest scores are observed in antimicrobial resistance (median: 43.09), followed by food security (median: 53.78), governance (median: 54.77), climate change (median: 64.12) and zoonotic diseases (median: 69.23). Globally, the scores of GOHI vary spatially, with the highest score in North America while lowest in sub-Saharan Africa. In addition, evidence shows associations between the socio-demographic profile of countries/territories and their GOHI performance in certain One Health scenarios. Conclusion: The objective of GOHI is to guide impactful strategies for enhancing capacity building in One Health. With advanced technology and an annually updated database, intensifying efforts to refine GOHI's data-mining methodologies become imperative. The goal is to offer profound insights into disparities and progressions in practical One Health implementation, particularly in anticipation of future pandemics.

LncRNA SNHG14 activates autophagy via regulating miR-493-5p/Mef2c axis to alleviate osteoporosis progression.

Osteoporosis is a progressive bone disease caused by impaired function of endogenous bone marrow-derived mesenchymal stem cells (BMSCs). Herein, we investigated the mechanism of lncRNA SNHG14 in osteoporosis progression. BMSCs were isolated from BALB/c mice. The osteogenic ability of BMSCs was assessed by Alkaline phosphatase (ALP) and Alizarin Red S Staining (ARS) staining. The interaction between miR-493-5p and SNHG14 or myocyte enhancer factor 2 C (Mef2c) was confirmed by dual-luciferase reporter assay. Bone histomorphometry changes were evaluated to analyze SNHG14'roles in osteoporosis in vivo. Our results illustrated SNHG14 and Mef2c levels were increased in a time-dependent manner in BMSCs, and miR-493-5p expression was decreased. SNHG14 knockdown inhibited osteogenic differentiation of BMSCs, and SNHG14 upregulation had the opposite effect. SNHG14 overexpression elevated bone mineral density and bone trabecular number, and alleviated osteoporosis progression in vivo. Mechanically, miR-493-5p was a target of SNHG14, and miR-493-5p targeted the Mef2c gene directly. SNHG14 overexpression reversed the inhibition of miR-493-5p on the osteogenic ability of BMSCs, and miR-493-5p silencing accelerated BMSCs osteogenesis by activating Mef2c-mediated autophagy to accelerate BMSCs osteogenesis. In short, SNHG14 activated autophagy via regulating miR-493-5p/Mef2c axis to alleviate osteoporosis progression, which might provide a new molecular target for osteoporosis treatment.

Recognizing and monitoring infectious sources of schistosomiasis by developing deep learning models with high-resolution remote sensing images.

BACKGROUND: China is progressing towards the goal of schistosomiasis elimination, but there are still some problems, such as difficult management of infection source and snail control. This study aimed to develop deep learning models with high-resolution remote sensing images for recognizing and monitoring livestock bovine, which is an intermediate source of Schistosoma japonicum infection, and to evaluate the effectiveness of the models for real-world application. METHODS: The dataset of livestock bovine's spatial distribution was collected from the Chinese National Platform for Common Geospatial Information Services. The high-resolution remote sensing images were further divided into training data, test data, and validation data for model development. Two recognition models based on deep learning methods (ENVINet5 and Mask R-CNN) were developed with reference to the training datasets. The performance of the developed models was evaluated by the performance metrics of precision, recall, and F1-score. RESULTS: A total of 50 typical image areas were selected, 1125 bovine objectives were labeled by the ENVINet5 model and 1277 bovine objectives were labeled by the Mask R-CNN model. For the ENVINet5 model, a total of 1598 records of bovine distribution were recognized. The model precision and recall were 81.9% and 80.2%, respectively. The F1 score was 0.81. For the Mask R-CNN mode, 1679 records of bovine objectives were identified. The model precision and recall were 87.3% and 85.2%, respectively. The F1 score was 0.87. When applying the developed models to real-world schistosomiasis-endemic regions, there were 63 bovine objectives in the original image, 53 records were extracted using the ENVINet5 model, and 57 records were extracted using the Mask R-CNN model. The successful recognition ratios were 84.1% and 90.5% for the respectively developed models. CONCLUSION: The ENVINet5 model is very feasible when the bovine distribution is low in structure with few samples. The Mask R-CNN model has a good framework design and runs highly efficiently. The livestock recognition models developed using deep learning methods with high-resolution remote sensing images accurately recognize the spatial distribution of livestock, which could enable precise control of schistosomiasis.

Field validation of different intervention modes for the potential transmission risk of schistosomiasis during post-transmission interruption phase.

OBJECTIVE: Precision interventions have been proposed in transmission-interrupted areas to further reduce the potential transmission risk of schistosomiasis. This study aimed to evaluate the effects of different interventions modes for potential transmission risk control. METHODS: Three groups of schistosomiasis-endemic villages were selected in Jiangling county, Hubei province. After baseline surveys in 2020, three intervention models were employed in 2021 and 2022. In Model 1, Oncomelania hupensis snail control in key settings and an integrated strategy with an emphasis on the infectious sources managing was employed. In Model 2, an integrated health education-led strategy with an emphasis on infectious source management was employed. In Model 3, only the integrated strategy with an emphasis on infectious source management was employed. The effects of the different intervention models were examined with multiple indicators after 2 years of intervention using the entropy-weighted technique for order of preference by similarity to ideal solution (TOPSIS), rank-sum ratio (RSR) and fuzzy combination model of entropy-weighted TOPSIS and RSR. RESULTS: Entropy-weighted TOPSIS modeling showed that the Ci values of Model 2 were 0.4434, 0.2759, and 0.3069 in the three pilot villages, Ci values were larger, with top comprehensive ranking. The results of the RSR method showed that the RSR values of Model 2 were 0.75, 0.708, and 0.736 in the three pilot villages, with top comprehensive ranking. The results from the fuzzy combination model of entropy-weighted TOPSIS and RSR showed that implementation of Model 2 resulted in the highest comprehensive ranking among the three models in the three pilot villages under Ci: RSR = 0.1: 0.9, Ci: RSR = 0.5: 0.5 and Ci: RSR = 0.9: 0.1. CONCLUSION: The integrated health education-led strategy with an emphasis on infectious source management was the optimal model to manage the risk of transmission of schistosomiasis during the post-transmission interruption phase.

Effect of Health Education on Schistosomiasis Control Knowledge, Attitude, and Practice after Schistosomiasis Blocking: Results of a Longitudinal Observational Study in the Field.

OBJECTIVES: Schistosomiasis is a zoonotic infectious disease that seriously harms people's physical and mental health. As early as 1985, the WHO suggested that health education and health promotion should be the focus of schistosomiasis prevention work. This study aimed to explore the effect of health education on controlling the risk of schistosomiasis transmission after schistosomiasis blocking and to provide a scientific basis for the further improvement of intervention strategies after schistosomiasis interruption in China and other endemic countries. METHODS: In Jiangling County, Hubei Province, China, one severe, one moderate, and one mildly endemic village were selected as the intervention group; two severe, two moderate, and two mildly endemic villages were selected as the control group. In towns with different epidemic types, a primary school was randomly selected for intervention. In September 2020, a baseline survey was carried out through a questionnaire survey to understand the knowledge, attitudes, and practices (KAP) of adults and students concerning schistosomiasis control. Next, two rounds of health education interventions for schistosomiasis control were carried out. The evaluation survey was conducted in September 2021 and the follow-up survey conducted in September 2022. RESULTS: Compared with the baseline survey, the qualified rate of the KAP on schistosomiasis prevention of the control group in the follow-up survey increased from 79.1% (584/738) to 81.0% (493/609) (p > 0.05); in the intervention group, the qualified rate of the KAP on schistosomiasis control increased from 74.9% (286/382) to 88.1% (260/295) (p < 0.001). In the baseline survey, the qualified rate of the KAP of the intervention group was lower than that of the control group, and in the follow-up survey, the qualified rate of the KAP of the intervention group was 7.2% higher than that of the control group (p < 0.05). Compared with the baseline survey, the accuracy rates of the KAP of the intervention group's adults were higher than those of the control group, with statistical significance (p < 0.001). Compared with the baseline survey, the qualified rate of the students' KAP in the follow-up survey increased from 83.8% (253/302) to 97.8% (304/311) (p < 0.001). In the follow-up survey, the accuracy rate of the knowledge, attitudes, and practices of the students was significantly different from the baseline accuracy (p < 0.001). CONCLUSION: a health education-led risk control model of schistosomiasis can significantly improve schistosomiasis control knowledge among adults and students, establishing correct attitudes and leading to the development of correct hygiene habits.

Spatiotemporal distribution of schistosomiasis transmission risk in Jiangling County, Hubei Province, P.R. China.

OBJECTIVE: This study aims to explore the spatiotemporal distribution of schistosomiasis in Jiangling County, and provide insights into the precise schistosomiasis control. METHODS: The descriptive epidemiological method and Joinpoint regression model were used to analyze the changes in infection rates of humans, livestock, snails, average density of living snails and occurrence rate of frames with snails in Jiangling County from 2005 to 2021. Spatial epidemiology methods were used to detect the spatiotemporal clustering of schistosomiasis transmission risk in Jiangling county. RESULTS: The infection rates in humans, livestock, snails, average density of living snails and occurrence rate of frames with snails in Jiangling County decreased from 2005 to 2021 with statistically significant. The average density of living snails in Jiangling County was spatially clustered in each year, and the Moran's I varied from 0.10 to 0.26. The hot spots were mainly concentrated in some villages of Xionghe Town, Baimasi Town and Shagang Town. The mean center of the distribution of average density of living snails in Jiangling County first moved from northwest to southeast, and then returned from southeast to northwest after 2014. SDE azimuth fluctuated in the range of 111.68°-124.42°. Kernal density analysis showed that the high and medium-high risk areas of Jiangling County from 2005 to 2021 were mainly concentrated in the central and eastern of Jiangling County, and the medium-low and low risk areas were mainly distributed in the periphery of Jiangling County. CONCLUSIONS: The epidemic situation of schistosomiasis decreased significantly in Jiangling County from 2005 to 2021, but the schistosomiasis transmission risk still had spatial clustering in some areas. After transmission interruption, targeted transmission risk intervention strategies can be adopted according to different types of schistosomiasis risk areas.