Scrub typhus is a mite-borne infectious disease caused by Orientia tsutsugamushi (previously called Rickettsia tsutsugamushi). The severity of this disease varies from only mild symptoms to death, and its manifestations are nonspecific. Therefore, clinicians may not correctly diagnose scrub typhus early enough for successful treatment. Reports of infections in travelers returning from Asia to their home countries are increasingly common. Thus, it is important that even clinicians in nonepidemic regions be alert for this disease. Here we describe the epidemiological aspects and clinical manifestations of scrub typhus encountered at a teaching hospital in Penghu, Taiwan, over the past 5 years. A total of 126 patients were confirmed to be positive for scrub typhus at the hospital from 2006 to 2010. All cases were confirmed by the Centers for Disease Control and Prevention or its contract laboratory through pathogen isolation and an indirect immunofluorescence assay. Medical records of these patients were reviewed, and demographic and clinical characteristics, laboratory data, seasonal data, geographic distribution, complications, and outcome were analyzed. The incidence of scrub typhus peaked in individuals aged 0–10 and 51–60 years, with the highest incidence among those ≤10 years of age. No significant difference was noted between sexes. Fever was the most common symptom (93.6%), followed by chills (23.8%), cough (18.3%), and headache (14.3%). Eschars were observed in 78 (61.9%) patients, with the axilla being the most frequent site (n=17; 21.8%). Most patients were retirees (n=63; 50%), followed by students (n=16; 12.7%). Patients were more likely to live in rural areas than urban areas. Scrub typhus was epidemic in the spring (April to June) and fall (October to December) in a bimodal distribution similar to that observed in Japan. Leukocytosis was not common, but most patients had abnormal C-reactive protein levels, thrombocytopenia, and elevated liver function test results. Residents of Penghu, particularly Makung City and Husi Township, as well as travelers to the region during the spring and fall seasons should be educated about the signs and symptoms of scrub typhus. All physicians who come into contact with individuals residing in or traveling to or from epidemic regions should remain alert about the manifestations of this disease.

Background We hypothesised that occupational exposures differently affect subtypes of adult-onset asthma. Objective We investigated potential relations between occupation and three subtypes of adult asthma, namely atopic asthma, non-atopic asthma and asthma–COPD overlap syndrome (ACOS). Methods This is a population-based case–control study of incident asthma among working-age adults living in Pirkanmaa Hospital District in Southern Finland. The determinant of interest was occupation at the time of diagnosis of asthma or the job that the subject had quit due to respiratory symptoms. Asthma was divided into three mutually exclusive subtypes on the basis of any positive IgE antibody (atopic and non-atopic asthma) and presence of persistent airways obstruction in spirometry (ACOS). We applied unconditional logistic regression analysis to estimate adjusted OR (aOR), taking into account gender, age and smoking. Results The following occupational groups showed significantly increased risk of atopic asthma: chemical industry workers (aOR 15.76, 95% CI 2.64 to 94.12), bakers and food processors (aOR 4.69, 95% CI 1.18 to 18.69), waiters (aOR 4.67, 95% CI 1.40 to 15.56) and those unemployed (aOR 3.06, 95% CI 1.52 to 6.17). The following occupations showed clearly increased risk of non-atopic asthma: metal workers (aOR 8.37, 95% CI 3.77 to 18.59) and farmers and other agricultural workers (aOR 2.36, 95% CI 1.10 to 5.06). Some occupational groups showed statistically significantly increased OR of ACOS: electrical and electronic production workers (aOR 30.6, 95% CI 6.10 to 153.35), fur and leather workers (aOR 16.41, 95% CI 1.25 to 215.85) and those retired (aOR 5.55, 95% CI 1.63 to 18.97). Conclusions Our results show that different occupations are associated with different subtypes of adult-onset asthma.

Al–Hg mediated chemoselective cleavage of the carbon–halogen bond of α-halocarboxylates allows for a facile and clean transformation of halohydrin aldol adducts into β-hydroxy acid derivatives with no evidence of dehydroxyhalogenation.

Purpose: This cohort study evaluated the effectiveness of noninvasive heart rate variability (HRV) analysis to assess the risk of cardiovascular disease over a period of 8 years. Methods: Personal and working characteristics were collected before biochemistry examinations and 5 min HRV tests from the Taiwan Bus Driver Cohort Study (TBDCS) in 2005. This study eventually identified 161 drivers with cardiovascular disease (CVD) and 627 without between 2005 and 2012. Estimation of the hazard ratio was analyzed by using the Cox proportional-hazards model. Results: Subjects with CVD had an overall lower standard deviation of NN intervals (SDNN) than their counterparts did. The SDNN index had a strong association with CVD, even after adjusting for risk factors. Using a median split for SDNN, the hazard ratio of CVD was 1.83 (95% CI = 1.10–3.04) in Model 1 and 1.87 (95% CI = 1.11–3.13) in Model 2. Furthermore, the low-frequency (LF) index was associated with a risk of CVD in the continuous approach. For hypertensive disease, the SDNN index was associated with increased risks in both the continuous and dichotomized approaches. When the root-mean-square of the successive differences (RMSSDs), high frequency (HF), and LF were continuous variables, significant associations with hypertensive disease were observed. Conclusions: This cohort study suggests that SDNN and LF levels are useful for predicting 8 year CVD risk, especially for hypertensive disease. Further research is required to determine preventive measures for modifying HRV dysfunction, as well as to investigate whether these interventions could decrease CVD risk among professional drivers.

Carbapenem-resistant Klebsiella pneumoniae bloodstream infections (CRKP BSI) are associated with high mortality rates and limited treatment options. This study compared the outcomes between ceftazidime-avibactam and carbapenem-based regimens in patients with nosocomial CRKP BSI. This is a single-center, retrospective, observational study that analyzed adult patients with nosocomial CRKP BSI from January 2018 to December 2023. Patients were divided into ceftazidime-avibactam (n = 47) and carbapenem-based regimen (n = 54) groups. Primary outcomes included 7-, 14-, 21-, and 28-day mortality rates, microbiological cure rates, and length of hospital stay. Multivariate analysis was performed to identify factors associated with mortality. The ceftazidime-avibactam group showed significantly lower 7-day mortality (8.51 % vs. 25.93 %, p = 0.043) and higher survival rates in Kaplan-Meier analysis (p = 0.021). Multivariate analysis revealed that ceftazidime-avibactam significantly reduced 7-day mortality (adjusted hazard ratio: 0.182, 95 % CI: 0.050-0.660, p = 0.010). Leukemia significantly increased both 7-day and all-cause mortality. Elevated white blood cell counts, and C-reactive protein levels were associated with increased mortality risk. Ceftazidime-avibactam demonstrated superior short-term survival benefits compared to carbapenem-based regimens in treating nosocomial CRKP BSI. However, long-term outcomes were more influenced by underlying conditions and inflammatory status.

During the coronavirus disease (COVID-19) pandemic, we admitted suspected or confirmed COVID-19 patients to our isolation wards between 2 March 2020 and 4 May 2020, following a well-designed and efficient assessment protocol. We included 217 patients suspected of COVID-19, of which 27 had confirmed COVID-19. The clinical characteristics of these patients were used to train artificial intelligence (AI) models such as support vector machine (SVM), decision tree, random forest, and artificial neural network for diagnosing COVID-19. When analyzing the performance of the models, SVM showed the highest sensitivity (SVM vs. decision tree vs. random forest vs. artificial neural network: 100% vs. 42.86% vs. 28.57% vs. 71.43%), while decision tree and random forest had the highest specificity (SVM vs. decision tree vs. random forest vs. artificial neural network: 88.37% vs. 100% vs. 100% vs. 94.74%) in the diagnosis of COVID-19. With the aid of AI models, physicians may identify COVID-19 patients earlier, even with few baseline data available, and segregate infected patients earlier to avoid hospital cluster infections and to ensure the safety of medical professionals and ordinary patients in the hospital.