Background. Antibiotic resistance is a global public health problem, leading to high mortality and treatment costs. To achieve more e cient treatment protocols and better patient recovery, the distribution and drug resistance of pathogens in our hospital were investigated, allowing signi cant clinical guidance for the use of antimicrobials. Methods. In this retrospective study (2017)(2018)(2019), 3482 positive samples were isolated from 43,981 specimens in 2017; 3750 positive specimens were isolated from 42,923 specimens in 2018; and 3839 positive pathogens were isolated from 46,341 specimens in 2019. ese samples were from various parts of the patients, including the respiratory tract, urine, blood, wound secretions, bile, and puncture uids. e distribution and antibiotic resistance of these isolated pathogens from the whole hospital were analyzed. Results. e results from pathogen isolation showed that Escherichia coli (12.8%), Staphylococcus aureus (11%), Klebsiella pneumoniae (10.8%), Pseudomonas aeruginosa (10.7%), and Acinetobacter baumannii (6.4%) represented the ve main pathogenic bacteria in our hospital. Pseudomonas aeruginosa (16.2% and 17.5%) occupied the largest proportion in the central intensive care unit (central ICU) and respiratory intensive care unit (RICU), while Acinetobacter baumannii (15.4%) was the most common pathogen in the emergency intensive care unit (EICU).e resistance rate of Escherichia coli to trimethoprim and minocycline was 100%, and the sensitivity rate to ertapenem, furantoin, and amikacin was above 90%.e resistance rate of Pseudomonas aeruginosa to all antibiotics, such as piperacillin and cipro oxacin, was under 40%. e sensitivity rate of Acinetobacter baumannii to tigecycline and minocycline was less than 30%, and the resistance rate to many drugs such as piperacillin, ceftazidime, and imipenem was above 60%. Extended-spectrum β-lactamases (ESBLs)-producing Klebsiella pneumoniae (ESBLs-KPN) and carbapenem-resistant Klebsiella pneumoniae (CRE-KPN), ESBLs-producing Escherichia coli (ESBLs-ECO) and carbapenem-resistant Escherichia coli (CRE-ECO), multidrug-resistant Acinetobacter baumannii (MDR-AB), multidrug-resistant Pseudomonas aeruginosa (MDR-PAE), and methicillin-resistant Staphylococcus aureus (MRSA) are all important multidrug-resistant bacteria found in our hospital. e resistance rate of ESBLsproducing Enterobacteriaceae to ceftriaxone and amcarcillin-sulbactam was above 95%. CRE Enterobacteriaceae bacteria showed the highest resistance to amcarcillin-sulbactam (97.1%), and the resistance rates of MDR-AB to cefotaxime, cefepime, and aztreonam were 100%.e resistance rates of MDR-PAE to ceftazidime, imipenem, and levo oxacin were 100%, and the sensitivity rate to polymyxin B was above 98%. e resistance rate of MRSA to oxacillin was 100%, and the sensitivity rate to linezolid and vancomycin was 100%. Conclusion. e distribution of pathogenic bacteria in di erent hospital departments and sample sources was markedly di erent. erefore, targeted prevention and control of key path...

Objective To discuss whether within-visit blood pressure variability (BPV) increases the risk of diabetic retinopathy (DR) after adjusting for the conventional metabolic risk factors and to evaluate the independent impact of BPV to DR in diabetic patients. Methods The study featured 625 patients with T2DM based on physical and biochemical tests. Among them, 66 patients had DR, and 66 HbA1c matched subjects without the condition (NDR group) were therefore handpicked from the remaining 559 participants in a ratio of 1:1. Three systolic BP (SBP) and diastolic BP (DBP) readings were recorded by physicians during a single medical assessment. Within-visit BPV for each subject was defined using the standard deviation of SBP (SBPSD) and DBP (DBPSD), coefficient of variation of SBP (SBPCV) and DBP (DBPCV). Results No significant differences were found in laboratory testing parameters as well as general clinical data between the two groups (P＞0.05), but cases of DR had higher SBPSD (2.88±2.41 vs 3.76±2.63, P=0.046) and SBPCV (1.92±1.63% vs 2.29±1.56%, P=0.036) than NDR subjects. SBPSD and SBPCV were both independent statistical indicators of DR after adjusting for age, gender, smoking history, alcohol consumption, obesity, history of hypertension, SBPM, DBPM, HbA1c and TC [OR: 1.188, 95%CI: 1.030-1.370, P=0.018 and OR: 1.271, 95%CI: 1.031-1.566, P=0.025, respectively]. Conclusion In T2DM patients, elevated within-visit SBPV associates strongly with DR in spite of no statistical difference in metabolic indicators. Even to the same degree of blood glucose control, within-visit SBPV is an independent indicator of DR.

Background: Fear of falling is a potential consequence of falling in older adults. Whether such fall-related psychological concerns in turn affect physical function? Especially those who have a history of falling but have not been diagnosed with anxiety, depression or both. This study aimed to clarify the effects of early psychological changes on the physical function of order patients. Methods: The 111 participants with falling history were divided into the poor physical function (PPF) group with SPPB≤9 and the good physical function (GPF) group with SPPB>10. Their physical function was assessed through 4-meter gait speed (4MGS), five times sit-to-stand test (FTSST), grip strength, and Timed Up and Go tests TUGT. Their mental state was assessed by the self-rating anxiety/depression scale (SAS/SDS). Results: (1) SAS/SDS scores were negatively correlated with the SPPB score, gait speed, and maximum grip strength (males). (2) Multivariate logistic regression analysis showed that the SPPB score was subject to such independent influence factors: cerebrovascular disease (OR =15.615; P =0.001), normal ratio of grip strength (OR =0.046; P =0.008), TUGT (OR =1.671; P <0.001), and SDS score (OR =1.155; P =0.005). (3) The area under the ROC curve was 0.699 (0.601, 0.797) for SAS score, with a sensitivity of 0.776 and a specificity of 0.547; the AUC was 0.694 (0.596, 0.792) for SDS score, with a sensitivity of 0.586 and a specificity of 0.755. Conclusions: This study showed that the higher SAS/SDS scores corresponded to poorer physical performance. SAS/SDS scores were to some extent predictive of physical function among older adults with a history of falls.