Urinary tract infections (UTIs) are among the most common bacterial infections worldwide, affecting millions annually and posing a significant global health concern. Traditional therapies for UTIs are becoming increasingly ineffective due to rising drug resistance and their tendency to disrupt the host's healthy microbiota, leading to further side effects. Consequently, there is an urgent need to develop alternative therapeutic agents that differ from conventional regimens and have fewer or no side effects. In this context, microbiome therapeutics offer a promising solution, given their demonstrated efficacy against various infectious diseases. Advances in scientific technology, particularly next-generation sequencing, have deepened our understanding of urinary microbiome dynamics, revealing a complex interplay within the urobiome that influences the onset and progression of UTIs. Uropathogenic bacteria do not solely cause UTIs; shifts in the composition of the urinary microbiome and interactions within the microbial community, known as host-microbiota interactions, also play a significant role. Although recent studies underscore the potential of targeting the urinary microbiome to manage UTIs and related complications, this field is still emerging and faces numerous regulatory and technical challenges. Further in-depth and comprehensive research is required to advance this pioneering concept into clinical practice.

Urinary tract infections (UTIs) are among the most common bacterial infections worldwide, affecting millions annually and posing a significant global health concern. Traditional therapies for UTIs are becoming increasingly ineffective due to rising drug resistance and their tendency to disrupt the host's healthy microbiota, leading to further side effects. Consequently, there is an urgent need to develop alternative therapeutic agents that differ from conventional regimens and have fewer or no side effects. In this context, microbiome therapeutics offer a promising solution, given their demonstrated efficacy against various infectious diseases. Advances in scientific technology, particularly next-generation sequencing, have deepened our understanding of urinary microbiome dynamics, revealing a complex interplay within the urobiome that influences the onset and progression of UTIs. Uropathogenic bacteria do not solely cause UTIs; shifts in the composition of the urinary microbiome and interactions within the microbial community, known as host-microbiota interactions, also play a significant role. Although recent studies underscore the potential of targeting the urinary microbiome to manage UTIs and related complications, this field is still emerging and faces numerous regulatory and technical challenges. Further in-depth and comprehensive research is required to advance this pioneering concept into clinical practice.

Urinary tract infections (UTIs) are among the most common bacterial infections worldwide, affecting millions annually and posing a significant global health concern. Traditional therapies for UTIs are becoming increasingly ineffective due to rising drug resistance and their tendency to disrupt the host's healthy microbiota, leading to further side effects. Consequently, there is an urgent need to develop alternative therapeutic agents that differ from conventional regimens and have fewer or no side effects. In this context, microbiome therapeutics offer a promising solution, given their demonstrated efficacy against various infectious diseases. Advances in scientific technology, particularly next-generation sequencing, have deepened our understanding of urinary microbiome dynamics, revealing a complex interplay within the urobiome that influences the onset and progression of UTIs. Uropathogenic bacteria do not solely cause UTIs; shifts in the composition of the urinary microbiome and interactions within the microbial community, known as host-microbiota interactions, also play a significant role. Although recent studies underscore the potential of targeting the urinary microbiome to manage UTIs and related complications, this field is still emerging and faces numerous regulatory and technical challenges. Further in-depth and comprehensive research is required to advance this pioneering concept into clinical practice.

Urinary tract infections (UTIs) are among the most common bacterial infections worldwide, affecting millions annually and posing a significant global health concern. Traditional therapies for UTIs are becoming increasingly ineffective due to rising drug resistance and their tendency to disrupt the host's healthy microbiota, leading to further side effects. Consequently, there is an urgent need to develop alternative therapeutic agents that differ from conventional regimens and have fewer or no side effects. In this context, microbiome therapeutics offer a promising solution, given their demonstrated efficacy against various infectious diseases. Advances in scientific technology, particularly next-generation sequencing, have deepened our understanding of urinary microbiome dynamics, revealing a complex interplay within the urobiome that influences the onset and progression of UTIs. Uropathogenic bacteria do not solely cause UTIs; shifts in the composition of the urinary microbiome and interactions within the microbial community, known as host-microbiota interactions, also play a significant role. Although recent studies underscore the potential of targeting the urinary microbiome to manage UTIs and related complications, this field is still emerging and faces numerous regulatory and technical challenges. Further in-depth and comprehensive research is required to advance this pioneering concept into clinical practice.

Urinary tract infections (UTIs) are among the most common bacterial infections worldwide, affecting millions annually and posing a significant global health concern. Traditional therapies for UTIs are becoming increasingly ineffective due to rising drug resistance and their tendency to disrupt the host's healthy microbiota, leading to further side effects. Consequently, there is an urgent need to develop alternative therapeutic agents that differ from conventional regimens and have fewer or no side effects. In this context, microbiome therapeutics offer a promising solution, given their demonstrated efficacy against various infectious diseases. Advances in scientific technology, particularly next-generation sequencing, have deepened our understanding of urinary microbiome dynamics, revealing a complex interplay within the urobiome that influences the onset and progression of UTIs. Uropathogenic bacteria do not solely cause UTIs; shifts in the composition of the urinary microbiome and interactions within the microbial community, known as host-microbiota interactions, also play a significant role. Although recent studies underscore the potential of targeting the urinary microbiome to manage UTIs and related complications, this field is still emerging and faces numerous regulatory and technical challenges. Further in-depth and comprehensive research is required to advance this pioneering concept into clinical practice.

Objectives: Severe mental illness has become one of the leading concerns for the cost of health services. This study analyzed the demographic characteristics and compared the patterns of medical health service use according to the diagnosis of severe mental illness, including schizophrenia spectrum disease, bipolar disease, and major depressive disorder. Methods: The data from the National Health Insurance Corporation were analyzed, selecting subjects diagnosed at least once for severe mental illness between 2014 and 2019. Severe mental illness included the following: schizophrenia, schizotypal, and delusional disorders (F20– 29); manic episodes and bipolar affective disorder (F30–31); and moderate depressive episodes with psychotic features and recurrent depressive disorder (F32.3–F33). The demographic factors and patterns of medical services, such as outpatient, hospitalization, and re-admission differences, were compared according to the diagnostic categories. Results: This study included 842459 patients, with 39.6% people in F20–F29, 33.7% in F32.3– F33, and 26.8% in the F30–F31 category. There were significant differences in gender, age, insurance type, Charlson Comorbidity Index score, and economic level according to the diagnostic categories. The engagement of medical health services also showed significant differences among the diagnostic categories. F32.3–F33 showed higher engagement of outpatients than the out-groups, while F20–F29 showed a higher admission rate. The hospitalization duration was significantly longer in F20–F29, and the re-admission rate after discharge within one year was significantly higher in the same group. Conclusion This paper reviewed the differences in medical care utilization among severe mental illness. The result emphasizes the need for a mental health care system broadening to the community, facilitating psychosocial intervention, and case management.

Purpose: This study was conducted to develop a convolutional neural network (CNN) algorithm that can diagnose cervical foraminal stenosis using oblique radiographs and evaluate its accuracy. Materials and Methods: A total of 997 patients who underwent cervical MRI and cervical oblique radiographs within a 3-month interval were included. Oblique radiographs were labeled as “foraminal stenosis” or “no foraminal stenosis” according to whether foraminal stenosis was present in the C2–T1 levels based on MRI evaluation as ground truth. The CNN model involved data augmentation, image preprocessing, and transfer learning using DenseNet161. Visualization of the location of the CNN model was performed using gradient-weight class activation mapping (Grad-CAM). Results: The area under the curve (AUC) of the receiver operating characteristic curve based on DenseNet161 was 0.889 (95% confidence interval, 0.851–0.927). The F1 score, accuracy, precision, and recall were 88.5%, 84.6%, 88.1%, and 88.5%, respectively.The accuracy of the proposed CNN model was significantly higher than that of two orthopedic surgeons (64.0%, p<0.001; 58.0%, p<0.001). Grad-CAM analysis demonstrated that the CNN model most frequently focused on the foramen location for the determination of foraminal stenosis, although disc space was also frequently taken into consideration. Conclusion A CNN algorithm that can detect neural foraminal stenosis in cervical oblique radiographs was developed. The AUC, F1 score, and accuracy were 0.889, 88.5%, and 84.6%, respectively. With the current CNN model, cervical oblique radiography could be a more effective screening tool for neural foraminal stenosis.

Lichens play crucial roles in the ecosystems, contributing to soil formation and nutrient cycling, and being used in biomonitoring efforts to assess the sustainability of ecosystems including air quality. Previous studies on heavy metal accumulation in lichens have mostly relied on manipulated environments, such as transplanted lichens, leaving us with a dearth of research on how lichens physiologically respond to heavy metal exposure in their natural habitats. To fill this knowledge gap, we investigated lichens from two of South Korea’s geo graphically distant regions, Gangwon Province and Jeju Island, and examined whether differ ence in ambient heavy metal concentrations could be detected through physiological variables, including chlorophyll damage, lipid oxidation, and protein content. The physio logical variables of lichens in response to heavy metals differed according to the collection area: Arsenic exerted a significant impact on chlorophyll degradation and protein content. The degree of fatty acid oxidation in lichens was associated with increased Cu concentra tions. Our research highlights the value of lichens as a bioindicator, as we found that even small variations in ambient heavy metal concentrations can be detected in natural lichens. Furthermore, our study sheds light on which physiology variables that can be used as indi cators of specific heavy metals, underscoring the potential of lichens for future ecology studies.

Background: This study aimed to investigate 1) long-term outcomes of deep brain stimulation (DBS), such as mortality after DBS as well as the causes of death, 2) demographic and socioeconomic factors influencing mortality, and 3) comorbidities affecting mortality after DBS in patients with Parkinson’s disease (PD). Methods: This study analyzed the National Health Insurance Service-National Health Information Database. Data on patients with PD diagnosis codes from 2002 to 2019 were extracted and analyzed. Data on the causes of death were obtained by linking the causes of death to data from Statistics Korea. The Kaplan-Meier method with the log-rank test was used for survival analysis. Multivariate Cox regression analyses were used to estimate hazard ratios (HRs) and their 95% confidence intervals. Regarding comorbidities such as PD dementia and fracture, which did not satisfy the assumption for the proportional HR, timedependent Cox analysis with the Mantel-Byar method was used. Results: From 2005 to 2017, among 156,875 patients diagnosed with PD in Korea, 1,079 patients underwent DBS surgery, and 251 (23.3%) had died by 2019. The most common cause of death (47.1%) was PD. In the multivariate Cox regression analysis, the higher the age at diagnosis and surgery, the higher the mortality rate. The men and medical aid groups had significantly higher mortality rates. PD dementia and fracture were identified as risk factors for mortality. Conclusion Older age at diagnosis and surgery, being male, the use of medical aid, and the comorbidity of dementia and fractures were associated with a higher risk of mortality after DBS in patients with PD. Neurologists should consider these risk factors in assessing the prognosis of PD patients undergoing DBS.