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.

Introduction: Metaplastic thymoma is an exceptionally rare thymic epithelial neoplasm, with fewer than 40 cases reported in the literature. It is defined by a distinctive biphasic proliferation of epithelioid epithelial nests and bland spindle cell fascicles, accompanied by a characteristic immunohistochemical profile. In limited cytology specimens, the lack of architectural context and potentially incomplete sampling can obscure the biphasic nature, leading to diagnostic difficulty. We describe a case initially interpreted as an epithelioid neoplasm on fine needle aspiration (FNA), with subsequent resection confirming metaplastic thymoma. Case Description: A 66-year-old male presented with an anterior mediastinal mass. FNA revealed a highly cellular specimen composed predominantly of polygonal tumor cells arranged in cohesive sheets. The cells displayed round to ovoid, hyperchromatic nuclei with moderate to marked pleomorphism, irregular nuclear membranes, conspicuous nucleoli, and abundant eosinophilic cytoplasm. Dystrophic calcifications were noted within a hemorrhagic background. An epithelioid tumor was favored on cytologic evaluation. The patient subsequently underwent video-assisted thoracoscopic surgery with excision of the mass. The specimen was well-encapsulated, lobulated, and traversed by fibrous septations. Microscopically, the tumor demonstrated a biphasic architecture: cohesive nests of epithelioid cells closely admixed with intersecting fascicles of bland spindle cells. Immature T-lymphocytes were not identified. Immunohistochemical staining showed diffuse cytokeratin and p40 positivity in the epithelioid component. In contrast, the spindle cell component lacked cytokeratin expression but exhibited strong vimentin positivity and patchy EMA reactivity. Both components were negative for CD5 and CD117. The Ki-67 proliferation index was low (1–2%). Discussion: Diagnosing metaplastic thymoma on cytology is challenging due to the tumor’s inherently biphasic architecture, which may not be adequately represented in limited FNA samples. In this case, the cytologic material disproportionately sampled the epithelioid component, masking the spindle cell element critical for diagnosis. Additionally, the lymphocyte-poor background mimicked type A or AB thymoma, further contributing to potential misclassification. Accurate diagnosis relies on careful integration of cytologic features with histologic, immunohistochemical, radiologic, and clinical data. Recognition of this rare entity is important, as its behavior differs substantially from other anterior mediastinal tumors, including thymic carcinoma and more aggressive thymoma subtypes. Conclusion Metaplastic thymoma can present as a purely epithelioid lesion on limited biopsy material, obscuring its hallmark biphasic nature. Awareness of this rare thymic neoplasm and correlation with resection findings are essential to avoid misdiagnosis. When accurately identified and completely excised, metaplastic thymoma carries an excellent prognosis.
Biopsy, Fine-Needle ; Thymoma

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.

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.

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.

Human ; Female ; Hemangioma, Capillary

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.