Objective:To investigate the relationship between sarcopenic obesity(SO), defined by various obesity indicators, and mortality risk in older adults based on a prospective cohort from multiple nursing homes.Methods:Sarcopenia was diagnosed according to the 2019 consensus of the Asian Working Group for Sarcopenia.Obesity was defined using six different indicators: waist circumference(WC), waist-hip ratio(WHR), waist-height ratio(WHTR), body mass index(BMI), visceral fat area(VFA), and percentage of body fat(PBF).A prospective cohort of adults aged 60 and above was established across 15 nursing homes in Zigong City, with annual follow-ups on survival status conducted over two consecutive years.Cox proportional hazards regression models were employed to analyze the association between SO, defined by different obesity indicators, and mortality risk, calculating hazard ratios( HR)and their 95% confidence intervals( CI). Results:A total of 695 older adults were included in the study, of whom 67.1% were male.During the 2-year follow-up period, 88 participants died.The prevalence of SO as defined by WC, WHR, WHTR, BMI, VFA, PBF was found to be 17.6%, 30.1%, 48.9%, 3.0%, 28.3%, and 58.7%, respectively.When SO was defined using WC, WHR, WHTR, BMI, and VFA, there was no statistically significant difference in mortality rates between the SO and non-SO groups.However, when defined by PBF, the SO group exhibited a significantly higher mortality rate compared to the non-SO group(16.9% vs.6.6%, P<0.01).Cox proportional hazards regression analysis revealed that, compared to the non-SO group, the SO group defined by PBF had a significantly increased mortality risk( HR=2.81, 95% CI: 1.67-4.73, P<0.001).After adjusting for potential confounding factors, the mortality risk for the SO group remained significantly higher than that of the non-SO group( HR=1.97, 95% CI: 1.14-3.38, P=0.015). Conclusions:The prevalence of SO varies significantly across different obesity indicators.SO defined by PBF is significantly associated with mortality risk in nursing home residents.This study provides new evidence for further optimizing the diagnostic criteria for SO in this population.

BACKGROUND: There is little literature describing the artificial intelligence (AI)-aided diagnosis of severe pneumonia (SP) subphenotypes and the association of the subphenotypes with the ventilatory treatment efficacy. The aim of our study is to illustrate whether clinical and biological heterogeneity, such as ventilation and gas-exchange, exists among patients with SP using chest computed tomography (CT)-based AI-aided latent class analysis (LCA). METHODS: This retrospective study included 413 patients hospitalized at Xinhua Hospital diagnosed with SP from June 1, 2015 to May 30, 2020. AI quantification results of chest CT and their combination with additional clinical variables were used to develop LCA models in an SP population. The optimal subphenotypes were determined though evaluating statistical indicators of all the LCA models, and clinical implications of them such as guiding ventilation strategies were further explored by statistical methods. RESULTS: The two-class LCA model based on AI quantification results of chest CT can describe the biological characteristics of the SP population well and hence yielded the two clinical subphenotypes. Patients with subphenotype-1 had milder infections ( P <0.001) than patients with subphenotype-2 and had lower 30-day ( P <0.001) and 90-day ( P <0.001) mortality, and lower in-hospital ( P = 0.001) and 2-year ( P <0.001) mortality. Patients with subphenotype-1 showed a better match between the percentage of non-infected lung volume (used to quantify ventilation) and oxygen saturation (used to reflect gas exchange), compared with patients with subphenotype-2. There were significant differences in the matching degree of lung ventilation and gas exchange between the two subphenotypes ( P <0.001). Compared with patients with subphenotype-2, those with subphenotype-1 showed a relatively better match between CT-based AI metrics of the non-infected region and oxygenation, and their clinical outcomes were effectively improved after receiving invasive ventilation treatment. CONCLUSIONS A two-class LCA model based on AI quantification results of chest CT in the SP population particularly revealed clinical heterogeneity of lung function. Identifying the degree of match between ventilation and gas-exchange may help guide decisions about assisted ventilation.
Humans ; Tomography, X-Ray Computed/methods* ; Male ; Female ; Retrospective Studies ; Middle Aged ; Artificial Intelligence ; Aged ; Pneumonia/diagnosis* ; Latent Class Analysis ; Adult

Objective To establish a rapid immunological detection method for MPT64 protein based on red microspheres and select highly-sensitive and highly-specific antibody pairs.Methods A His-tagged prokaryotic expression vector was constructed for expression of MPT64 protein that was used to immunize New Zealand white rabbits after purification and validation.Peripheral blood mononuclear cells(PBMCs)were isolated from the rabbits,and antigen-specific B cells expressing antibodies were sorted using single B-cell flow cytometry.mRNA in B cells was reverse-transcribed into cDNA,and paired antibody heavy-and light-chain sequences were amplified via nested PCR.Expression vectors were constructed,and recombinant antibodies were produced in Expi293F cells.Fluorescent immunochromatography was employed to screen for matched antibody pairs.The selected antibodies were used to establish a rapid detection method based on red microsphere immunochromatography.Results Ten high-affinity monoclonal antibodies against MPT64 were generated.Two antibody pairs were selected for MPT64 immunodetection that reached a sensitivity of 0.0125 ng/mL.Conclusion High-affinity rabbit monoclonal antibodies against MPT64 are obtained via single B-cell technology,and a rapid red microspheres-based immunodetection method is established,enabling highly sensitive detection of Mycobacterium tuberculosis MPT64 protein.

Diabetes is one of the major chronic non-communicable diseases in China. This article discusses the progress of diabetes prevention and control under the leadership of the government and how to integrate the National Diabetes Prevention and Control Center (DPCC) into the work of primary medical and health institutions in Yunnan Province, providing reference and experience for diabetes prevention and control efforts in the central and western regions of China.

Objective To propose a novel method for verifying the isocenter in radiotherapy based on markers and conduct a preliminary test. Methods A feasibility experiment was conducted on wooden box phantom for radiotherapy resetting. Fifteen groups of displacement data were randomly generated,corresponding to the position deviations of the isocenter in the radiotherapy plan relative to the original isocenter. According to each set of displacement data,with the aid of movable lasers and a CT scanning couch,CT scanning was performed with two sets of markers (3 per set) affixed to the phantom which were corresponding to the original and treatment isocenters,respectively. In the Eclipse treatment planning system,the coordinate data of the original and treatment isocenters were manually verified,and the difference of coordinate data was calculated to obtain the actual displacement value. The treatment isocenter position was finally confirmed by comparing with the actual displacement. In addition,the study attempts to use threshold segmentation algorithm to automatically detect metal markers and obtain coordinate values of the original isocenter on patient-positioned CT images. In the wooden box experiment,the absolute value of the difference between the actual displacement value and the planned displacement value (?d) was used to represent the position accuracy of treatment isocenter,and the deviation value obtained with threshold segmentation algorithm for isocenter detection was ?s. Results The ?d in the X (left-right),Y (superior-inferior) and Z (anterior-superior) directions was (0.83±0.37) mm,0 (0,0.5) mm and (0.45±0.29) mm,respectively. The ?s in the X,Y and Z directions was (0.46±0.22) mm,0 (0,0.5) mm and (0.33±0.29) mm,respectively. The mean values of ?s in 3 directions were all lower than 2 mm,within the range of permissible clinical positioning error. Conclusion The method of automatically verifying treatment isocenter position based on markers is feasible,and the study provides a useful reference for radiotherapy resetting using CT simulator.

Diabetes is one of the major chronic non-communicable diseases in China. This article discusses the progress of diabetes prevention and control under the leadership of the government and how to integrate the National Diabetes Prevention and Control Center (DPCC) into the work of primary medical and health institutions in Yunnan Province, providing reference and experience for diabetes prevention and control efforts in the central and western regions of China.

Objective:To establish a pure water direct dilution-inductively coupled plasma mass spectrometry (ICP-MS) detection method for rapid determination of urinary iodine.Methods:Pure water was used to directly dilute the urine samples. The washing solution was 5.0 g/L ascorbic acid, the internal standard solution was 5.0 g/L ascorbic acid and 100 μg/L 128Te, the standard solution was prepared with the solution of lyophilized urine iodine biological component analysis reference material. The method was evaluated in terms of linear range, detection limit, quantification limit, precision and method comparision experiment. Results:The linear correlation coefficient of the standard curve for iodine concentration range from 0 to 50.0 μg/L was 0.999 7, with a detection limit of 0.2 μg/L and a quantification limit of 0.6 μg/L. The spiked recovery rates of low, medium, and high concentration iodine standard solutions added to actual urine samples were 100.8%, 99.1% and 99.7%, respectively, with relative standard deviations of 0.8%, 1.3% and 1.6%, respectively. There was no statistically significant difference ( t = - 0.14, P = 0.890) between the results of measuring actual urine and assessment urine using this method and "Determination of Iodine in Urine-Part 2: Inductively Coupled Plasma Mass Spectrometry (WS/T 107.2-2016)". Conclusions:We have successfully established a pure water direct dilution-ICP-MS method for determining urinary iodine. This method provides accurate and highly sensitive results, making it suitable for sudden public health emergencies and large-scale clinical measurement of urinary iodine.

Type 2 diabetes mellitus(T2DM)is a chronic metabolic disorder caused by a combination of genetic and environmental factors.The epigenetic transcriptional modifications after RNA transcription can affect gene expression and glucose homeostasis.N6-methyladenine(m6A),as an RNA methylation modification,plays an important role in the pathogenesis of T2DM.This article reviews the research progress of m6A methylation in T2DM.

Objective:To establish a pure water direct dilution-inductively coupled plasma mass spectrometry (ICP-MS) detection method for rapid determination of urinary iodine.Methods:Pure water was used to directly dilute the urine samples. The washing solution was 5.0 g/L ascorbic acid, the internal standard solution was 5.0 g/L ascorbic acid and 100 μg/L 128Te, the standard solution was prepared with the solution of lyophilized urine iodine biological component analysis reference material. The method was evaluated in terms of linear range, detection limit, quantification limit, precision and method comparision experiment. Results:The linear correlation coefficient of the standard curve for iodine concentration range from 0 to 50.0 μg/L was 0.999 7, with a detection limit of 0.2 μg/L and a quantification limit of 0.6 μg/L. The spiked recovery rates of low, medium, and high concentration iodine standard solutions added to actual urine samples were 100.8%, 99.1% and 99.7%, respectively, with relative standard deviations of 0.8%, 1.3% and 1.6%, respectively. There was no statistically significant difference ( t = - 0.14, P = 0.890) between the results of measuring actual urine and assessment urine using this method and "Determination of Iodine in Urine-Part 2: Inductively Coupled Plasma Mass Spectrometry (WS/T 107.2-2016)". Conclusions:We have successfully established a pure water direct dilution-ICP-MS method for determining urinary iodine. This method provides accurate and highly sensitive results, making it suitable for sudden public health emergencies and large-scale clinical measurement of urinary iodine.