Objective To investigate the distribution and antimicrobial resistance profiles of clinically isolated Haemophilus influenzae and Moraxella catarrhalis in hospitals across China from 2015 to 2021,and provide evidence for rational use of antimicrobial agents.Methods Data of H.influenzae and M.catarrhalis strains isolated from 2015 to 2021 in CHINET program were collected for analysis,and antimicrobial susceptibility testing was performed by disc diffusion method or automated systems according to the uniform protocol of CHINET.The results were interpreted according to the CLSI breakpoints in 2022.Beta-lactamases was detected by using nitrocefin disk.Results From 2015 to 2021,a total of 43 642 strains of Haemophilus species were isolated,accounting for 2.91％of the total clinical isolates and 4.07％of Gram-negative bacteria in CHINET program.Among the 40 437 strains of H.influenzae,66.89％were isolated from children and 33.11％were isolated from adults.More than 90％of the H.influenzae strains were isolated from respiratory tract specimens.The prevalence of β-lactamase was 53.79％in H.influenzae strains.The H.influenzae strains isolated from children showed higher resistance rate than the strains isolated from adults.Overall,779 strains of H.influenzae did not produce β-lactamase but were resistant to ampicillin(BLNAR).Beta-lactamase-producing strains showed significantly higher resistance rates to these antimicrobial agents than the β-lactamase-nonproducing strains.Of the 16 191 M.catarrhalis strains,80.06％were isolated from children and 19.94％isolated from adults.M.catarrhalis strains were mostly susceptible to both amoxicillin-clavulanic acid and cefuroxime,evidenced by resistance rate lower than 2.0％.Conclusions The emergence of antibiotic-resistant H.influenzae due to β-lactamase production poses a challenge for clinical anti-infective treatment.Therefore,it is very important to implement antibiotic resistance surveillance for H.influenzae and guide rational antibiotic use.All local clinical microbiology laboratories should actively improve antibiotic susceptibility testing and strengthen antibiotic resistance surveillance for H.influenzae.

Objective To investigate the distribution and antimicrobial resistance profiles of clinically isolated Haemophilus influenzae and Moraxella catarrhalis in hospitals across China from 2015 to 2021,and provide evidence for rational use of antimicrobial agents.Methods Data of H.influenzae and M.catarrhalis strains isolated from 2015 to 2021 in CHINET program were collected for analysis,and antimicrobial susceptibility testing was performed by disc diffusion method or automated systems according to the uniform protocol of CHINET.The results were interpreted according to the CLSI breakpoints in 2022.Beta-lactamases was detected by using nitrocefin disk.Results From 2015 to 2021,a total of 43 642 strains of Haemophilus species were isolated,accounting for 2.91％of the total clinical isolates and 4.07％of Gram-negative bacteria in CHINET program.Among the 40 437 strains of H.influenzae,66.89％were isolated from children and 33.11％were isolated from adults.More than 90％of the H.influenzae strains were isolated from respiratory tract specimens.The prevalence of β-lactamase was 53.79％in H.influenzae strains.The H.influenzae strains isolated from children showed higher resistance rate than the strains isolated from adults.Overall,779 strains of H.influenzae did not produce β-lactamase but were resistant to ampicillin(BLNAR).Beta-lactamase-producing strains showed significantly higher resistance rates to these antimicrobial agents than the β-lactamase-nonproducing strains.Of the 16 191 M.catarrhalis strains,80.06％were isolated from children and 19.94％isolated from adults.M.catarrhalis strains were mostly susceptible to both amoxicillin-clavulanic acid and cefuroxime,evidenced by resistance rate lower than 2.0％.Conclusions The emergence of antibiotic-resistant H.influenzae due to β-lactamase production poses a challenge for clinical anti-infective treatment.Therefore,it is very important to implement antibiotic resistance surveillance for H.influenzae and guide rational antibiotic use.All local clinical microbiology laboratories should actively improve antibiotic susceptibility testing and strengthen antibiotic resistance surveillance for H.influenzae.

Objective To investigate the relationship between appendicular skeletal muscle composition(muscle mass and muscle index)and glycosylated hemoglobin A1c(HbA1c)level in type 2 diabetes mellitus(T2DM)patients.Methods A total of 459 adult T2DM patients hospitalized in the Department of Endocrinology,Tangdu Hospital of Air Force Medical University from April 2021 to June 2022 were selected as the research objects.Bioelectrical impedance analysis was used to evaluate the body composition of the patients.The patients were divided into two groups according to HbA1c level:standard glycation group(HbA1c≤7.0%,n=145)and unqualified glycation group(HbA1c>7.0%,n=314).The two groups'general data,biochemical indexes,muscle content,fat content and other body composition were analyzed.Spearman correlation analysis and multiple logistic regression analysis were used to analyze the relationship between body composition and glycemic control in type 2 diabetic patients.Results The appendicular skeletal muscle mass and its index(ASMI)of the patients in the standard glycation group were better than those in the unqualified glycation group(P<0.05).However,there were no significant differences in gender,history of antidiabetic drugs and body fat(body fat content,limb fat content and visceral fat content)between the two groups(P>0.05).Spearman correlation analysis showed that appendicular skeletal muscle mass and its index were negatively correlated with HbA1c(r=-0.158,P=0.001;r=-0.187,P<0.001).Logistic regression analysis showed that upper limb skeletal muscle mass(OR=3.570,95%CI 2.293-5.559)and lower limb skeletal muscle mass(OR=1.297,95%CI 1.146-1.468)were independent protective factors for achieving glycation standard in HbA1c group.Conclusions The skeletal muscle mass of limbs is a protective factor for reaching the standard of HbA1c.With the increase of skeletal muscle mass of limbs,the level of HbA1c gradually decreases.Among them,the increase of upper limb muscle mass has a stronger correlation with reaching the standard of HbA1c.

Objective To construct an artificial intelligence assisted diagnosis model based on deep learning for dynamically recognizing gastric lesions and their locations under gastroscopy in real time,and to evaluate its ability to detect and recognize gastric lesions and their locations.Methods The gastroscopy videos of 104 patients in our hospital was retrospectively analyzed,and the video frames were manually annotated.The annotated picture frames of lesion category were divided into the training set and the validation set according to the ratio of 8∶2,and the annotated picture frames of location category were divided into the training set and the validation set according to the patient sources at the ratio of 8∶2.These sets were utilized for training and validating the respective models.YoloV4 model was used for the training of lesion recognition,and ResNet152 model was used for the training of location recognition.The accuracy,sensitivity,specificity,positive predictive value,negative predictive value and location recognition accuracy of the auxiliary diagnostic model were evaluated.Results A total of 68 351 image frames were annotated,with 54 872 frames used as the training set,including 41 692 frames for lesion categories and 13 180 frames for location categories.The validation set consisted of 13 479 frames,comprising 10 422 frames for lesion categories and 3 057 frames for location categories.The lesion recognition model achieved an overall accuracy of 98.8%,with a sensitivity of 96.6%,specificity of 99.3%,positive predictive value of 96.3%,and negative predictive value of 99.3% in validation set.Meanwhile,the location recognition model demonstrated an top-5 accuracy of 87.1% .Conclusion The artificial intelligence assisted diagnosis model based on deep learning for real-time dynamic recognition of gastric lesions and their locations under gastroscopy has good ability in lesion detection and location recognition,and has great clinical application prospects.

Objective To investigate the ultrasonography,high-risk factors and pregnancy outcomes related to pernicious placenta previa(PPP)and neonatal asphyxia.Methods The clinical data of 144 pregnant women with PPP from Tongji Hospital,Tongji Medical College,Huazhong University of Science and Technology from May 2016 to July 2020 were retrospectively analyzed.Depending on the neonatal condition,they were divided into non-asphyxia group(n=91)and asphyxia group(n=53).Mainly by prenatal ultrasound ex-amination and intraoperative diagnosis,the high-risk factors of neonatal asphyxia were analyzed with univariate and multivariate Logistic regression analysis,and the neonatal outcomes were compared between the two groups.Results Compared with the non-asphyxia group,the incidence of placenta dominated by anterior wall,complete placenta previa,premature delivery,polyhydramnios and placenta percreta were higher in the asphyxia group,and the differences were statistically significant(P＜0.05).Multivariate Logistic regression analysis showed that placenta dominated by anterior wall,premature delivery,polyhydramnios and placenta percreta were independent risk factors for neonatal asphyxia(P＜0.05,OR＞1).Compared with the non-asphyxia group,the incidence of neonatal admission was higher with lower gestational age and birth weight in the asphyxia group(P＜0.05).Conclusion The risk factors related to PPP and neonatal asphyxia are placenta dominated by anterior wall,premature delivery,polyhydramnios and placenta percreta.Pregnancy outcomes related to PPP and neonatal asphyxia are worse.

As artificial intelligence technology rapidly advances, its deployment within the medical sector presents substantial ethical challenges. Consequently, it becomes crucial to create a standardized, transparent, and secure framework for processing medical data. This includes setting the ethical boundaries for medical artificial intelligence and safeguarding both patient rights and data integrity. This consensus governs every facet of medical data handling through artificial intelligence, encompassing data gathering, processing, storage, transmission, utilization, and sharing. Its purpose is to ensure the management of medical data adheres to ethical standards and legal requirements, while safeguarding patient privacy and data security. Concurrently, the principles of compliance with the law, patient privacy respect, patient interest protection, and safety and reliability are underscored. Key issues such as informed consent, data usage, intellectual property protection, conflict of interest, and benefit sharing are examined in depth. The enactment of this expert consensus is intended to foster the profound integration and sustainable advancement of artificial intelligence within the medical domain, while simultaneously ensuring that artificial intelligence adheres strictly to the relevant ethical norms and legal frameworks during the processing of medical data.
Artificial Intelligence/legislation & jurisprudence* ; Humans ; Consensus ; Computer Security/standards* ; Confidentiality/ethics* ; Informed Consent/ethics*

Objective:To study influence of Status-Background-Assessment-Recommendation(SBAR)communi-cation mode intervention on patients with heart failure(HF).Methods:According to intervention method,a total of 187 HF patients treated in our hospital were divided into routine intervention group(n=96,received routine in-tervention based on routine treatment)and SBAR group(n=91,received SBAR communication mode intervention based on routine treatment).Both groups were intervened for six months.General clinical data,LVEF,left ven-tricular end-diastolic volume(LVEDV),left ventricular end-systolic volume(LVESV),scores of Morisky medi-cation compliance scale and the medical outcomes study 36-item short-form heath survey(SF-36),patients'satis-faction and incidence rate of major adverse cardiovascular events(MACE)within six-month follow-up were com-pared between two groups.Results:Compared with routine intervention group,after intervention,there were sig-nificant rise in LVEF[(53.44±2.11)%vs.(57.72±1.80)%],scores of Morisky medication compliance scale[(2.00±0.77)scores vs.(3.63±1.07)scores]and SF-36[(68.83±7.62)scores vs.(76.81±9.68)scores],and significant reductions in LVEDV[(132.63±5.59)ml vs.(119.15±6.89)ml]and LVESV[(64.36±5.82)ml vs.(56.62±4.21)ml]in SBAR group(P=0.001 all).Patients'satisfaction of SBAR group was significantly higher than that of routine intervention group(89.01%vs.73.96%,χ2=6.958,P=0.008),and incidence rate MACE during follow-up was significantly lower than the latter(5.49%vs.15.63%,LogRankχ2=5.043,P= 0.025).Conclusion:SBAR communication mode intervention can significantly improve heart function,medication compliance and quality of life in patients with heart failure,with high patients'satisfaction.

BackgroundPerinatal depression seriously affects the physical and mental health of pregnant women， such as affecting their role transition， identity， and family relationships， etc. In severe case， it can even lead to suicidal behavior， causing a heavy burden on pregnant women and their families. A hierarchical management model centered on pregnant women， involving collaboration of families， communities， and hospitals， facilitates comprehensive and dynamic management of perinatal depression. ObjectiveTo evaluate the application effect of "Internet +" hospital-community-family trinity linkage management model on perinatal depression in pregnant women， in order to provide a reference for the clinical intervention. Methods80 pregnant women who established medical records from the Department of Obstetrics and Gynecology of Mianyang Third People's Hospital from January to December 2022， with Edinburgh Postnatal Depression Scale （EPDS） score>9， were selected as the research objects. According to the random number table method， they were divided into a study group and a control group， each group consisting of 40 cases. Both groups received routine nursing intervention in the pregnant women's school of obstetrics and gynecology outpatient department before delivery， and in the study room of the department of obstetrics and gynecology after being admitted to the hospital for delivery. After discharge， they received routine follow-up until 42 days postpartum. The study group received the "Internet +" hospital-community-family ternary linkage management on the basis. EPDS， Pittsburgh Sleep Quality Index （PSQI）， Generic Quality of Life Inventory （GQOLI-74） and Nursing Satisfaction questionnaire were assessed before intervention and 42 days postpartum. ResultsAfter intervention， the EPDS score and PSQI score of the study group were lower than those of the control group （F=42.823， 60.453， P<0.05）， GQOLI-74 score and nursing satisfaction were higher than those of the control group （F=198.902， χ²=5.165， P<0.05） . Conclusion"Internet +" hospital-community-family trinity linkage management model may help to improve the severity of perinatal depression symptoms， improve the quality of sleep and life， and increase the satisfaction of pregnant women.［Funded by Mianyang Health Scientific Research Commission Project （number， 202134）］

In the outbreak of COVID-19,triage procedures based on epidemiology were implemented in a local hospital in Changsha to control the transmission of SARS-CoV-2 and avoid healthcare-associated infection.This re-trospective study analyzed the data collected during the triage period and found that COVID-19 patients were en-riched 7 folds into the Section A designated for patients with obvious epidemiological history.On the other side,nearly triple amounts of visits were received at the Section B for patients without obvious epidemiological history.8 COVID-19 cases were spotted out of 247 suspected patients.More than 50％of the suspected patients were submi-tted to multiple rounds of nucleic acid analysis for SARS-CoV-2 infection.Of the 239 patients who were diagnosed as negative of the virus infection,188 were successfully revisited and none was reported as COVID-19 case.Of the 8 COVID-19 patients,3 were confirmed only after multiple rounds of nucleic acid analysis.Besides comorbidities,delayed sharing of epidemiological history added complexity to the diagnosis in practice.The triaging experience and strategy will be helpful for the control of infectious diseases in the future.

Objective: To understand the epidemiological characteristics of bacillary dysentery with multiple-onset in Henan province from 2005 to 2020. Methods: The reported cases of bacillary dysentery (including confirmed cases and clinically diagnosed cases) in Henan Province from January 2005 to December 2020 were collected through China's National Disease Supervision Information Management System. The main information included gender, age, home address, date of onset and date of diagnosis. The interval between two episodes of the same case was more than 15 days, which was judged as two episodes. The incidence characteristics of bacillary dysentery patients with two or more cases in Henan Province from 2005 to 2020 were analyzed, and the regional distribution map of cases was drawn using ArcGIS software. Results: From 2005 to 2020, a total of 250 430 cases of bacillary dysentery were reported in Henan Province, with a cumulative incidence rate of 228.66/100 000. There were 2 342 cases with two or more attacks. The incidence of recurrent cases of bacillary dysentery increased year by year (χ²_trend=2 932.28, P<0.001). There was no significant difference in the incidence of two or more cases of different sexes (χ²=0.39, P=0.540). There was significant difference in the incidence among different age groups (χ²=438.40, P<0.001). The incidence of two or more cases in the 60-69 age group was relatively high (1.70%). The shortest time interval between the onset of the disease was 16 days, and the longest was 5 579 days, with M (Q₁, Q₃) about 428 (237, 843) days. Compared with healthy people, those with a history of bacterial diseases had a higher risk of developing bacillary dysentery (RR: 4.12, 95%CI: 3.95‒4.29). Conclusion: The proportion of patients with multiple-onset shows an increasing trend, and there is an age difference.
Humans ; Dysentery, Bacillary/diagnosis* ; Incidence