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Abstract Background Climate change has raised scientific interest in examining the associations of weather conditions with adverse health effects, while most studies determined human thermal stress using ambient air temperature rather than the thermophysiological index. Objectives To evaluate the association between emergency ambulance dispatches (EADs) related to cardiovascular causes and heat/cold stress in Shenzhen, a city in southern China, with the aim of providing new insights for local policymakers. Methods A time series analysis using ambulance dispatch data of cardiovascular diseases in Shenzhen, China (2013–2019) was conducted. A quasi-Poisson nonlinear distributed lag model was applied to explore the relationship between emergency ambulance dispatches (EADs) due to cardiovascular causes and thermal stress (determined by Universal Thermal Climate Index, UTCI). Attributable fractions were estimated to identify which UTCI ranges have a greater health impact. Results The relationship between UTCI and EADs due to cardiovascular diseases exhibits a reverse J-shaped curve. The effects of cold stress were immediate and long-lasting, whereas the effects of heat stress were non-significant. Compared with the optimal equivalent temperature (71st percentile of UTCI, 29.22 °C), the relative risks for cumulative (0–21 days) exposures to cold stress (1st percentile, − 0.13 °C; 5th percentile, 7.68 °C) were 1.55 (95%CI:1.28,1.88) and 1.44 (95%CI:1.22,1.69), respectively. Thermal (cold and heat) stress was responsible for 10.81% (95%eCI: 5.67%,15.43%) of EADs for cardiovascular diseases, with 9.46% (95%eCI: 3.98%,14.40%) attributed to moderate cold stress (2.5th ~ 71st percentile). Greater susceptibility to cold stress was observed for males and the elderly. Heat stress showed harmful effects in the warm season. Conclusions Our results demonstrated that cold exposure elevates the risk of EADs for cardiovascular causes in Shenzhen, and moderate cold stress cause the highest burden of ambulance dispatches. Health authorities should consider effective adaptation strategies and interventions responding to cold stress to reduce the morbidity of cardiovascular diseases.

ObjectiveTo determine whether the diagnostic ability of the newly designed hierarchical fuzzy diagnosis method is consistent with that of headache experts for probable migraine (PM) and probable tension-type headache (PTTH).BackgroundClinical decision support systems (CDSS) are computer systems designed to help doctors to make clinician decisions by information technology, and have proven to be effective in improving headache diagnosis by making medical knowledge readily available to users in some studies. However, one serious drawback is that the CDSS lacks the ability to deal with some fuzzy boundaries of the headache features utilized in diagnostic criteria, which might be caused by patients’ recall bias and subjective bias.MethodsA hybrid mechanism of rule-based reasoning and hierarchical fuzzy diagnosis method based on International Classification of Headache Disorders, 3rd edition (ICHD-3) was designed and then validated by a retrospective study with 325 consecutive patients and a prospective study with 380 patients who were clinically diagnosed with migraine and TTH at the headache clinic of Chinese PLA General Hospital.ResultsThe results of the diagnostic test in the retrospective study indicated that the fuzzy-based CDSS can be used in the diagnosis of migraine without aura (MO) (sensitivity 97.71%, specificity 100%), TTH (sensitivity 98.57%, specificity 100%), PM (sensitivity 91.25%, specificity 98.75%) and PTTH (sensitivity 90.91%, specificity 99.63%). While in the prospective study, the diagnostic performances were MO (sensitivity 91.62%, specificity 96.52%), TTH (sensitivity 92.17%, specificity 95.47%), PM (sensitivity 85.48%, specificity 98.11%) and PTTH (sensitivity 87.50%, specificity 98.60%). Cohen’s kappa values for the consistency test were 0.984 ± 0.018 (MO), 0.991 ± 0.018 (TTH), 0.916 ± 0.051 (PM), 0.932 ± 0.059 (PTTH) in the retrospective study and 0.884 ± 0.047 (MO), 0.870 ± 0.055 (TTH), 0.853 ± 0.073 (PM), 0.827 ± 0.118 (PTTH) in the prospective study, which indicated good consistency with the fuzzy-based CDSS and the gold standard (p

Primary tinnitus is a disabling disease with an unknown pathogenesis and a high incidence rate in China. Its diagnosis and treatment are complex and difficult to control. Although many treatments are available for primary tinnitus, their efficacy is often unsatisfactory. This paper proposes a new diagnosis and treatment method using knowledge graphs, and an intelligent assistant decision system is developed. To support diagnosis, a knowledge graph is created as a decision support tool using traditional Chinese medicine (TCM). Based on the knowledge graph, a model for the syndrome differentiation of tinnitus in TCM is built. At tinnitus treatment, an intelligent recommandation model for pentatonic music using knowledge graph based heterogeneous label propagation is then used to provide patients with personalized treatment plans. According to evaluation results, the proposed method achieves an accuracy of 87.1 % in tinnitus diagnosis. Compared with the control group, the recommended pentatonic music had a more obvious effect, and the efficacy of the five types of tinnitus was increased by 33.34 %, 33.33 %, 20 %, 26.67 %, 33.34 %, respectively. The system developed in this paper will help clinicians improve the diagnosis and treatment of tinnitus while reducing unnecessary medical expenses and offering significant social and economic benefits.

BackgroundTinnitus diagnosis poses a challenge in otolaryngology owing to an extremely complex pathogenesis, lack of effective objectification methods, and factor-affected diagnosis. There is currently a lack of explainable auxiliary diagnostic tools for tinnitus in clinical practice. ObjectiveThis study aims to develop a diagnostic model using an explainable artificial intelligence (AI) method to address the issue of low accuracy in tinnitus diagnosis. MethodsIn this study, a knowledge graph–based tinnitus diagnostic method was developed by combining clinical medical knowledge with electronic medical records. Electronic medical record data from 1267 patients were integrated with traditional Chinese clinical medical knowledge to construct a tinnitus knowledge graph. Subsequently, weights were introduced, which measured patient similarity in the knowledge graph based on mutual information values. Finally, a collaborative neighbor algorithm was proposed, which scored patient similarity to obtain the recommended diagnosis. We conducted 2 group experiments and 1 case derivation to explore the effectiveness of our models and compared the models with state-of-the-art graph algorithms and other explainable machine learning models. ResultsThe experimental results indicate that the method achieved 99.4% accuracy, 98.5% sensitivity, 99.6% specificity, 98.7% precision, 98.6% F1-score, and 99% area under the receiver operating characteristic curve for the inference of 5 tinnitus subtypes among 253 test patients. Additionally, it demonstrated good interpretability. The topological structure of knowledge graphs provides transparency that can explain the reasons for the similarity between patients. ConclusionsThis method provides doctors with a reliable and explainable diagnostic tool that is expected to improve tinnitus diagnosis accuracy.

Abstract Accurate segmentation of brain gliomas (BG) is a crucial and challenging task for effective treatment planning in BG therapy. This study presents the weight loss function and dropout U‐Net with ConvNeXt block (WD‐UNeXt), which precisely segments BG from few shot MRI. The ConvNeXt block, which comprises the main body of the network, is a structure that can extract more detailed features from images. The weight loss function addresses the issue of category imbalance, thereby enhancing the network's ability to achieve more precise segmentation. The training set of BraTS2019 was used to train the network and apply it to test data. Dice similarity coefficient (DSC), sensitivity (Sen), specificity (Spec) and Hausdorff distance (HD) were used to assess the performance of the method. The experimental results demonstrate that the DSC of whole tumour, tumour core and enhancing tumour reached 0.934, 0.911 and 0.851, respectively. Sen of the sub‐regions achieved 0.922, 0.911 and 0.867. Spec and HD reached 1.000, 1.000, 1.000 and 3.224, 2.990, 2.844, respectively. Compared with the performance of state‐of‐the‐art methods, the DSC and HD of WD‐UNeXt were improved to varying degrees. Therefore, this method has considerable potential for the segmentation of BG.

IntroductionThe precise associations between temperature-related indices and mental and behavioral disorders (MBDs) have yet to be fully elucidated. Our study aims to ascertain the most effective temperature-related index and assess its immediate impact on emergency ambulance dispatches (EADs) due to MBDs in Shenzhen, China.MethodsEADs data and meteorological data from January 1, 2013, to December 31, 2020, in Shenzhen were collected. Distributed lag non-linear models (DLNMs) were utilized to examine the non-linear and lagged effects of temperature-related indices on EADs due to MBDs. The Quasi Akaike Information criterion (QAIC) was used to determine the optimal index after standardizing temperature-related indices. After adjusting for confounding factors in the model, we estimated the immediate and cumulative effects of temperature on EADs due to MBDs.ResultsThe analysis of short-term temperature effects on EADs due to MBDs revealed Humidex as the most suitable index. Referring to the optimal Humidex (3.2th percentile, 12.00°C), we observed a significant effect of Humidex over the threshold (34.6th percentile, 26.80°C) on EADs due to MBDs at lag 0–5. The cumulative relative risks for high temperature (90th percentile, 41.90°C) and extreme high temperature (99th percentile, 44.20°C) at lag 0–5 were 1.318 (95% CI: 1.159–1.499) and 1.338 (95% CI: 1.153–1.553), respectively. No significant cold effect was observed on EADs due to MBDs.ConclusionHigh Humidex was associated with more EADs due to MBDs in subtropical regions. Health authorities should implement effective measures to raise public awareness of risks related to high temperature and protect vulnerable populations.

Abstract Background Although headache disorders are common, the current diagnostic approach is unsatisfactory. Previously, we designed a guideline-based clinical decision support system (CDSS 1.0) for diagnosing headache disorders. However, the system requires doctors to enter electronic information, which may limit widespread use. Methods In this study, we developed the updated CDSS 2.0, which handles clinical information acquisition via human–computer conversations conducted on personal mobile devices in an outpatient setting. We tested CDSS 2.0 at headache clinics in 16 hospitals in 14 provinces of China. Results Of the 653 patients recruited, 18.68% (122/652) were suspected by specialists to have secondary headaches. According to “red-flag” responses, all these participants were warned of potential secondary risks by CDSS 2.0. For the remaining 531 patients, we compared the diagnostic accuracy of assessments made using only electronic data firstly. In Comparison A, the system correctly recognized 115/129 (89.15%) cases of migraine without aura (MO), 32/32 (100%) cases of migraine with aura (MA), 10/10 (100%) cases of chronic migraine (CM), 77/95 (81.05%) cases of probable migraine (PM), 11/11 (100%) cases of infrequent episodic tension-type headache (iETTH), 36/45 (80.00%) cases of frequent episodic tension-type headache (fETTH), 23/25 (92.00%) cases of chronic tension-type headache (CTTH), 53/60 (88.33%) cases of probable tension-type headache (PTTH), 8/9 (88.89%) cases of cluster headache (CH), 5/5 (100%) cases of new daily persistent headache (NDPH), and 28/29 (96.55%) cases of medication overuse headache (MOH). In Comparison B, after combining outpatient medical records, the correct recognition rates of MO (76.03%), MA (96.15%), CM (90%), PM (75.29%), iETTH (88.89%), fETTH (72.73%), CTTH (95.65%), PTTH (79.66%), CH (77.78%), NDPH (80%), and MOH (84.85%) were still satisfactory. A patient satisfaction survey indicated that the conversational questionnaire was very well accepted, with high levels of satisfaction reported by 852 patients. Conclusions The CDSS 2.0 achieved high diagnostic accuracy for most primary and some secondary headaches. Human–computer conversation data were well integrated into the diagnostic process, and the system was well accepted by patients. The follow-up process and doctor–client interactions will be future areas of research for the development of CDSS for headaches.

Abstract Objective This study aimed to understand the demographics, clinical characteristics, and psychoacoustic status of subjective tinnitus patients to explore the factors associated with acute and chronic tinnitus in the general hospitals of Shanghai. Methods A cross‐sectional study was conducted to investigate the clinical characteristics and psychoacoustic status of subjective tinnitus patients with history greater than 1 month. Data were collected during January 2021 and January 2022 from eight general hospitals in five districts of Shanghai, China. All patients accepted questionnaires and acoustic examination, then SPSS 22.0 and GraphPad Prism 8.0.2 were applied for data analysis. Results Among the 359 tinnitus patients, 126 cases were diagnosed with acute tinnitus, 58 cases were subacute tinnitus, and 175 cases were chronic tinnitus. Patients with acute and chronic tinnitus differed in terms of side of tinnitus, hearing loss, frequency of tinnitus, severity of tinnitus, anxiety, depression, and sleep status. Different characteristics were included in the multivariate ordinal logistic regression analysis. Persistent tinnitus (OR = 2.00, p = .008), high frequency hearing loss (OR = 4.77, p

Abstract Background The aim of the study was to investigate whether MwoA and MwA are different manifestations of a single disease, distinct clinical entities, or located at two poles of a spectrum. Methods In this cross-sectional study, 5438 patients from 10 hospitals in China were included: 4651 were diagnosed with migraine without aura (MwoA) and 787 with migraine with aura (MwA). We used a validated standardized electronic survey to collect multidimensional data on headache characteristics and evaluated the similarities and differences between migraine subtypes. To distinguish migraine subtypes, we employed correlational analysis, factor analysis of mixed data (FAMD), and decision tree analysis. Results Compared to MwA, MwoA had more severe headaches, predominantly affected females, were more easily produced by external factors, and were more likely to have accompanying symptoms and premonitory neck stiffness. Patients with MwA are heterogeneous, according to correlation analysis; FAMD divided the subjects into three clear clusters. The majority of the differences between MwoA and MwA were likewise seen when typical aura with migraine headache (AWM) and typical aura with non-migraine headache (AWNM) were compared. Furthermore, decision trees analysis revealed that the chaotic MwA data reduced the decision tree’s accuracy in distinguishing MwoA from MwA, which was significantly increased by splitting MwA into AWM and AWNM. Conclusions The clinical phenomics of headache phenotype varies gradually from MwoA to AWM and AWNM, and AWM is a mid-state between MwoA and AWNM. We tend to regard migraine as a spectrum disorder, and speculate that different migraine subtypes have different “predominant regions” that generate attacks.

In order to solve the vibration and reliability problems of timing belt transmission system of a 2.0L VVT engine，the mechanical model of timing drive system is simplified from the point of view of force balance，and the external excitation factors affecting the vibration of timing drive system are quickly found. Through the methods of reducing the fluctuation of camshafts loads，reducing VVT adjusting shock，optimizing belt tensioner internal structure，reducing the vibration of timing belt transmission system and timing tensioner swing，the issues of wear failure of timing tensioner and belt are solved. The key index of the vibration of the timing transmission system is determined，and the reliability test of the engine is verified.

Early recognition of abnormal gait enables physicians to determine a prompt rehabilitation plan for patients for the most effective treatment and care. The Kinect depth sensor can easily collect skeleton data describing the position of joints in the human body. However, the default human skeleton model of Kinect includes an excessive number of many joints, which limits the accuracy of the gait recognition methods and increases the computational resources required. In this study, we propose an optimized human skeleton model for the Kinect system and streamline the joints using a center-of-mass calculation. We integrate several techniques to propose an end-to-end, spatial–temporal, joint attention graph convolutional network (STJA-GCN) architecture. We conducted experiments with a fivefold cross-validation on two common datasets of information on abnormal gaits to evaluate the performance of the proposed method. The results show that the STJA-GCN achieved 93.17 and 92.08% accuracy on the two datasets, and compared to the original spatial–temporal graph convolutional network (ST-GCN), the recognition accuracy increases by 9.22 and 20.65%, respectively. Overall, the results demonstrate that the STJA-GCN can accurately recognize abnormal gaits and, thus, can support low-cost rehabilitation assessments at community hospitals or in patients’ homes.