OBJECTIVES: The coronavirus disease 2019 (COVID-19) pandemic had direct effects on population health through infection and morbidity, as well as indirect effects on population mental health. We estimated the network structure of post-traumatic stress symptoms (PTSS) and depressive symptoms throughout the pandemic in Korea and aimed to identify the most central and bridging symptoms. METHODS: Participants aged 30–64 years completed mental health surveys across 3 phases of the COVID-19 pandemic: March 2020 (n=1,925), February–March 2021 (n=1,754), and December 2021–January 2022 (n=1,595). Using PTSS and depressive symptom data, we conducted network analyses, and the primary measures of symptom importance (centrality) were expected influence and bridge expected influence. RESULTS: In the comorbidity network, although the most central symptoms fluctuated over the course of the pandemic, sleep problems were consistently identified as the most influential bridge symptoms throughout. The symptom network structure differed between the subacute and chronic phases of the pandemic. CONCLUSIONS We found evidence of changes in the network structure of PTSS and depressive symptoms, even as sleep problems retained a consistent role as a bridging symptom. Although overall network structures varied across phases of the pandemic, the bridging role of sleep-related symptoms remained consistently strong, suggesting that sleep problems may represent a general and enduring mechanism underlying PTSS–depression comorbidity. During future pandemics, prompt screening for sleep problems may help prevent the development of comorbidity between PTSS and depressive symptoms.

Objective: The variety and efficacy of biomarkers available that may be used objectively to diagnose major depressive disorder (MDD) in adults are unclear. This systematic review aims to identify and evaluate the variety of objective markers used to diagnose MDD in adults. Methods: The search strategy was applied via PubMed and PsycINFO over the past 10 years (2013–2023) to capture the latest available evidence supporting the use of biomarkers to diagnose MDD. Data was reported through narrative synthesis. Results: Forty-two studies were included in the review. Findings were synthesised based on the following measures: blood, neuroimagingeurophysiology, urine, dermatological, auditory, vocal, cerebrospinal fluid and combinatory—and evaluated based on its sensitivity/specificity and area under the curve values. The best predictors of blood (MYT1 gene), neuroimagingeurophysiological (5-HT1A auto-receptor binding in the dorsal and median raphe), urinary (combined albumin, AMBP, HSPB, APOA1), cerebrospinal fluid-based (neuron specific enolase, microRNA) biomarkers were found to be closely linked to the pathophysiology of MDD. Conclusion A large variety of biomarkers were available to diagnose MDD, with the best performing biomarkers intrinsically related to the pathophysiology of MDD. Potential for future research lies in investigating the joint sensitivity of the best performing biomarkers identified via machine learning methods and establishing the causal effect between these biomarkers and MDD.

Human ; Female ; Hemangioma, Capillary

Objective: The variety and efficacy of biomarkers available that may be used objectively to diagnose major depressive disorder (MDD) in adults are unclear. This systematic review aims to identify and evaluate the variety of objective markers used to diagnose MDD in adults. Methods: The search strategy was applied via PubMed and PsycINFO over the past 10 years (2013–2023) to capture the latest available evidence supporting the use of biomarkers to diagnose MDD. Data was reported through narrative synthesis. Results: Forty-two studies were included in the review. Findings were synthesised based on the following measures: blood, neuroimagingeurophysiology, urine, dermatological, auditory, vocal, cerebrospinal fluid and combinatory—and evaluated based on its sensitivity/specificity and area under the curve values. The best predictors of blood (MYT1 gene), neuroimagingeurophysiological (5-HT1A auto-receptor binding in the dorsal and median raphe), urinary (combined albumin, AMBP, HSPB, APOA1), cerebrospinal fluid-based (neuron specific enolase, microRNA) biomarkers were found to be closely linked to the pathophysiology of MDD. Conclusion A large variety of biomarkers were available to diagnose MDD, with the best performing biomarkers intrinsically related to the pathophysiology of MDD. Potential for future research lies in investigating the joint sensitivity of the best performing biomarkers identified via machine learning methods and establishing the causal effect between these biomarkers and MDD.

Objective: The variety and efficacy of biomarkers available that may be used objectively to diagnose major depressive disorder (MDD) in adults are unclear. This systematic review aims to identify and evaluate the variety of objective markers used to diagnose MDD in adults. Methods: The search strategy was applied via PubMed and PsycINFO over the past 10 years (2013–2023) to capture the latest available evidence supporting the use of biomarkers to diagnose MDD. Data was reported through narrative synthesis. Results: Forty-two studies were included in the review. Findings were synthesised based on the following measures: blood, neuroimagingeurophysiology, urine, dermatological, auditory, vocal, cerebrospinal fluid and combinatory—and evaluated based on its sensitivity/specificity and area under the curve values. The best predictors of blood (MYT1 gene), neuroimagingeurophysiological (5-HT1A auto-receptor binding in the dorsal and median raphe), urinary (combined albumin, AMBP, HSPB, APOA1), cerebrospinal fluid-based (neuron specific enolase, microRNA) biomarkers were found to be closely linked to the pathophysiology of MDD. Conclusion A large variety of biomarkers were available to diagnose MDD, with the best performing biomarkers intrinsically related to the pathophysiology of MDD. Potential for future research lies in investigating the joint sensitivity of the best performing biomarkers identified via machine learning methods and establishing the causal effect between these biomarkers and MDD.

Thermal analysis technology has emerged as a pivotal tool for the identification and quality control of traditional Chinese medicine （TCM） owing to its advantages of high sensitivity and capability for simultaneous multi-parameter detection. The application progress on thermogravimetric analysis （TGA）, differential thermal analysis （DTA）, and differential scanning calorimetry （DSC） in four key areas: authenticity identification of herbal medicines, optimization of processing techniques, evaluation of extract thermal stability, and construction of quality evaluation systems were summarized. Thermal analysis technology enables rapid authentication of medicinal materials by establishing a thermal fingerprint. When integrated with hyphenated techniques （e.g., FTIR and GC-MS）, it facilitates in-depth analysis of compositional differences in complex matrices. In Future, the development of thermal analysis databases and multi-technology integration will be expected to further promote the standardization of TCM quality control.

Objective: The variety and efficacy of biomarkers available that may be used objectively to diagnose major depressive disorder (MDD) in adults are unclear. This systematic review aims to identify and evaluate the variety of objective markers used to diagnose MDD in adults. Methods: The search strategy was applied via PubMed and PsycINFO over the past 10 years (2013–2023) to capture the latest available evidence supporting the use of biomarkers to diagnose MDD. Data was reported through narrative synthesis. Results: Forty-two studies were included in the review. Findings were synthesised based on the following measures: blood, neuroimagingeurophysiology, urine, dermatological, auditory, vocal, cerebrospinal fluid and combinatory—and evaluated based on its sensitivity/specificity and area under the curve values. The best predictors of blood (MYT1 gene), neuroimagingeurophysiological (5-HT1A auto-receptor binding in the dorsal and median raphe), urinary (combined albumin, AMBP, HSPB, APOA1), cerebrospinal fluid-based (neuron specific enolase, microRNA) biomarkers were found to be closely linked to the pathophysiology of MDD. Conclusion A large variety of biomarkers were available to diagnose MDD, with the best performing biomarkers intrinsically related to the pathophysiology of MDD. Potential for future research lies in investigating the joint sensitivity of the best performing biomarkers identified via machine learning methods and establishing the causal effect between these biomarkers and MDD.

Objective: The variety and efficacy of biomarkers available that may be used objectively to diagnose major depressive disorder (MDD) in adults are unclear. This systematic review aims to identify and evaluate the variety of objective markers used to diagnose MDD in adults. Methods: The search strategy was applied via PubMed and PsycINFO over the past 10 years (2013–2023) to capture the latest available evidence supporting the use of biomarkers to diagnose MDD. Data was reported through narrative synthesis. Results: Forty-two studies were included in the review. Findings were synthesised based on the following measures: blood, neuroimagingeurophysiology, urine, dermatological, auditory, vocal, cerebrospinal fluid and combinatory—and evaluated based on its sensitivity/specificity and area under the curve values. The best predictors of blood (MYT1 gene), neuroimagingeurophysiological (5-HT1A auto-receptor binding in the dorsal and median raphe), urinary (combined albumin, AMBP, HSPB, APOA1), cerebrospinal fluid-based (neuron specific enolase, microRNA) biomarkers were found to be closely linked to the pathophysiology of MDD. Conclusion A large variety of biomarkers were available to diagnose MDD, with the best performing biomarkers intrinsically related to the pathophysiology of MDD. Potential for future research lies in investigating the joint sensitivity of the best performing biomarkers identified via machine learning methods and establishing the causal effect between these biomarkers and MDD.

Background/Aims: Gastroesophageal reflux disease (GERD) is typically managed based on the clinical phenotype. We evaluated the efficacy and safety of potassium-competitive acid blockers (PCABs) in patients with various clinical GERD phenotypes. Methods: Core databases were searched for studies comparing PCABs and proton pump inhibitors (PPIs) in clinical GERD phenotypes of erosive reflux disease (ERD), non-erosive reflux disease (NERD), PPI-resistant GERD and night-time heartburn. Additional analysis was performed based on disease severity and drug dosage, and pooled efficacy was calculated. Results: In 9 randomized controlled trials (RCTs) evaluating the initial treatment of ERD, the risk ratio for healing with PCABs versus PPIs was 1.09 (95% CI, 1.04-1.13) at 2 weeks and 1.03 (95% CI, 1.00-1.07) at 8 weeks, respectively. PCABs exhibited a significant increase in both initial and sustained healing of ERD compared to PPIs in RCTs, driven particularly in severe ERD (Los Angeles grade C/D).In 3 NERD RCTs, PCAB was superior to placebo in proportion of days without heartburn. Observational studies on PPI-resistant symptomatic GERD reported symptom frequency improvement in 86.3% of patients, while 90.7% showed improvement in PPIresistant ERD across 5 observational studies. Two RCTs for night-time heartburn had different endpoints, limiting meta-analysis. Pronounced hypergastrinemia was observed in patients treated with PCABs. Conclusions Compared to PPIs, PCABs have superior efficacy and faster therapeutic effect in the initial and maintenance therapy of ERD, particularly severe ERD. While PCABs may be an alternative treatment option in NERD and PPI-resistant GERD, findings were inconclusive in patients with night-time heartburn.

Objective To evaluate the curative effect ofeliminating flatulence and laxative cream on patients with advanced hepatocellular carcinoma and opioid-associated constipation（OIC）. Methods 120 patients with advanced liver cancer complicated with OIC who were treated at the Third Affiliated Hospital of Naval Medical University from June 2021 to December 2022 were selected as the study subjects. The patients were divided into a control group（lactulose+conventional treatment）and an experimental group（eliminating flatulence and laxative cream + conventional treatment）using a randomized numerical table method. Two groups were compared in terms of defecation, quality of life, and comprehensive post-treatment evaluation（economic cost, number of occurrences of diarrhea, and whether or not there was a change in the dosage of opioids used）. Results After 28 d of intervention, both groups showed better results in relieving OIC（P<0.05）, and the experimental group was significantly better than the control group in terms of the quality of life of patients with advanced hepatocellular carcinoma, the economic cost and the number of diarrhea（P<0.05）. Conclusion In the treatment of OIC in patients with advanced hepatocellular carcinoma, constipation could be relieved by using both topicaleliminating flatulence and laxative cream and oral lactulose solution. Among them, antieliminating flatulence and laxative cream was more acceptable to patients and superior in terms of quality of life and economic cost, which could be a better choice for improving patient satisfaction and safety.