Objectives: This study aimed to explore factors influencing satisfaction with medical services among medically underserved populations at the free medical clinic, providing data to improve free medical services for these populations. Methods: We employed a descriptive correlational study design involving 112 individuals (aged 19 years and older) from medically underserved populations who visited the clinic. Data were collected through face-to-face surveys from September to October 2023, and statistical analyses (t-tests, analysis of variance, Pearson correlation, and hierarchical multiple regression) were used to identify key predictors of satisfaction. Results: Perceived support from healthcare providers emerged as the strongest predictor ofsatisfaction with medical services, demonstrating a significant positive association. While socialsupport was positively correlated with perceived support from healthcare providers, it did not independently predict satisfaction. Conclusion These findings underscore the importance of healthcare provider and social supportin increasing satisfaction with medical services among medically underserved populations.Developing tailored healthcare programs and specialized healthcare provider training are essential strategies to improve healthcare access and outcomes for these vulnerable groups.

Objectives: This study aimed to explore factors influencing satisfaction with medical services among medically underserved populations at the free medical clinic, providing data to improve free medical services for these populations. Methods: We employed a descriptive correlational study design involving 112 individuals (aged 19 years and older) from medically underserved populations who visited the clinic. Data were collected through face-to-face surveys from September to October 2023, and statistical analyses (t-tests, analysis of variance, Pearson correlation, and hierarchical multiple regression) were used to identify key predictors of satisfaction. Results: Perceived support from healthcare providers emerged as the strongest predictor ofsatisfaction with medical services, demonstrating a significant positive association. While socialsupport was positively correlated with perceived support from healthcare providers, it did not independently predict satisfaction. Conclusion These findings underscore the importance of healthcare provider and social supportin increasing satisfaction with medical services among medically underserved populations.Developing tailored healthcare programs and specialized healthcare provider training are essential strategies to improve healthcare access and outcomes for these vulnerable groups.

Objectives: This study aimed to explore factors influencing satisfaction with medical services among medically underserved populations at the free medical clinic, providing data to improve free medical services for these populations. Methods: We employed a descriptive correlational study design involving 112 individuals (aged 19 years and older) from medically underserved populations who visited the clinic. Data were collected through face-to-face surveys from September to October 2023, and statistical analyses (t-tests, analysis of variance, Pearson correlation, and hierarchical multiple regression) were used to identify key predictors of satisfaction. Results: Perceived support from healthcare providers emerged as the strongest predictor ofsatisfaction with medical services, demonstrating a significant positive association. While socialsupport was positively correlated with perceived support from healthcare providers, it did not independently predict satisfaction. Conclusion These findings underscore the importance of healthcare provider and social supportin increasing satisfaction with medical services among medically underserved populations.Developing tailored healthcare programs and specialized healthcare provider training are essential strategies to improve healthcare access and outcomes for these vulnerable groups.

Background: Recurrent gynecological clear cell carcinoma (rGCCC) has a low objective response rate (ORR) to chemotherapy. Previous preclinical and clinical data suggest a potential synergy between immune checkpoint inhibitors and bevacizumab in rGCCC.Dostarlimab, a humanized monoclonal antibody targeting programmed cell death protein 1 (PD-1), combined with the anti-angiogenic bevacizumab, presents a novel therapeutic approach. This study will investigate the efficacy of dostarlimab +/− bevacizumab in rGCCC. Methods DOVE is a global, multicenter, international, open-label, randomized phase 2 study of dostarlimab +/− bevacizumab with standard chemotherapy in rGCCC. We will enroll 198 patients with rGCCC and assign them to one of three groups in a 1:1:1 ratio: arm A (dostarlimab monotherapy), B (dostarlimab + bevacizumab), and C (investigator’s choice of chemotherapy [weekly paclitaxel, pegylated liposomal doxorubicin, doxorubicin, or gemcitabine]). Patients with disease progression in arm A or C will be allowed to cross over to arm B. Stratification factors include prior bevacizumab use, prior lines of therapy (1 vs. >1), and primary site (ovarian vs. non-ovarian). Key inclusion criteria are histologically proven recurrent or persistent clear cell carcinoma of the ovary, endometrium, cervix, vagina, or vulva; up to five prior lines of therapy; disease progression within 12 months after platinumbased chemotherapy; and measurable disease. Key exclusion criteria are prior treatment with an anti–PD-1, anti–programmed death-ligand 1, or anti–programmed death-ligand 2 agent.The primary endpoint is progression-free survival determined by investigators. Secondary endpoints are ORR, disease control rate, clinical benefit rate, progression-free survival 2, overall survival, and toxicity. Exploratory objectives include immune biomarkers.

Background: Recurrent gynecological clear cell carcinoma (rGCCC) has a low objective response rate (ORR) to chemotherapy. Previous preclinical and clinical data suggest a potential synergy between immune checkpoint inhibitors and bevacizumab in rGCCC.Dostarlimab, a humanized monoclonal antibody targeting programmed cell death protein 1 (PD-1), combined with the anti-angiogenic bevacizumab, presents a novel therapeutic approach. This study will investigate the efficacy of dostarlimab +/− bevacizumab in rGCCC. Methods DOVE is a global, multicenter, international, open-label, randomized phase 2 study of dostarlimab +/− bevacizumab with standard chemotherapy in rGCCC. We will enroll 198 patients with rGCCC and assign them to one of three groups in a 1:1:1 ratio: arm A (dostarlimab monotherapy), B (dostarlimab + bevacizumab), and C (investigator’s choice of chemotherapy [weekly paclitaxel, pegylated liposomal doxorubicin, doxorubicin, or gemcitabine]). Patients with disease progression in arm A or C will be allowed to cross over to arm B. Stratification factors include prior bevacizumab use, prior lines of therapy (1 vs. >1), and primary site (ovarian vs. non-ovarian). Key inclusion criteria are histologically proven recurrent or persistent clear cell carcinoma of the ovary, endometrium, cervix, vagina, or vulva; up to five prior lines of therapy; disease progression within 12 months after platinumbased chemotherapy; and measurable disease. Key exclusion criteria are prior treatment with an anti–PD-1, anti–programmed death-ligand 1, or anti–programmed death-ligand 2 agent.The primary endpoint is progression-free survival determined by investigators. Secondary endpoints are ORR, disease control rate, clinical benefit rate, progression-free survival 2, overall survival, and toxicity. Exploratory objectives include immune biomarkers.

Objectives: This study aimed to explore factors influencing satisfaction with medical services among medically underserved populations at the free medical clinic, providing data to improve free medical services for these populations. Methods: We employed a descriptive correlational study design involving 112 individuals (aged 19 years and older) from medically underserved populations who visited the clinic. Data were collected through face-to-face surveys from September to October 2023, and statistical analyses (t-tests, analysis of variance, Pearson correlation, and hierarchical multiple regression) were used to identify key predictors of satisfaction. Results: Perceived support from healthcare providers emerged as the strongest predictor ofsatisfaction with medical services, demonstrating a significant positive association. While socialsupport was positively correlated with perceived support from healthcare providers, it did not independently predict satisfaction. Conclusion These findings underscore the importance of healthcare provider and social supportin increasing satisfaction with medical services among medically underserved populations.Developing tailored healthcare programs and specialized healthcare provider training are essential strategies to improve healthcare access and outcomes for these vulnerable groups.

Background: Recurrent gynecological clear cell carcinoma (rGCCC) has a low objective response rate (ORR) to chemotherapy. Previous preclinical and clinical data suggest a potential synergy between immune checkpoint inhibitors and bevacizumab in rGCCC.Dostarlimab, a humanized monoclonal antibody targeting programmed cell death protein 1 (PD-1), combined with the anti-angiogenic bevacizumab, presents a novel therapeutic approach. This study will investigate the efficacy of dostarlimab +/− bevacizumab in rGCCC. Methods DOVE is a global, multicenter, international, open-label, randomized phase 2 study of dostarlimab +/− bevacizumab with standard chemotherapy in rGCCC. We will enroll 198 patients with rGCCC and assign them to one of three groups in a 1:1:1 ratio: arm A (dostarlimab monotherapy), B (dostarlimab + bevacizumab), and C (investigator’s choice of chemotherapy [weekly paclitaxel, pegylated liposomal doxorubicin, doxorubicin, or gemcitabine]). Patients with disease progression in arm A or C will be allowed to cross over to arm B. Stratification factors include prior bevacizumab use, prior lines of therapy (1 vs. >1), and primary site (ovarian vs. non-ovarian). Key inclusion criteria are histologically proven recurrent or persistent clear cell carcinoma of the ovary, endometrium, cervix, vagina, or vulva; up to five prior lines of therapy; disease progression within 12 months after platinumbased chemotherapy; and measurable disease. Key exclusion criteria are prior treatment with an anti–PD-1, anti–programmed death-ligand 1, or anti–programmed death-ligand 2 agent.The primary endpoint is progression-free survival determined by investigators. Secondary endpoints are ORR, disease control rate, clinical benefit rate, progression-free survival 2, overall survival, and toxicity. Exploratory objectives include immune biomarkers.

Objectives: This study aimed to explore factors influencing satisfaction with medical services among medically underserved populations at the free medical clinic, providing data to improve free medical services for these populations. Methods: We employed a descriptive correlational study design involving 112 individuals (aged 19 years and older) from medically underserved populations who visited the clinic. Data were collected through face-to-face surveys from September to October 2023, and statistical analyses (t-tests, analysis of variance, Pearson correlation, and hierarchical multiple regression) were used to identify key predictors of satisfaction. Results: Perceived support from healthcare providers emerged as the strongest predictor ofsatisfaction with medical services, demonstrating a significant positive association. While socialsupport was positively correlated with perceived support from healthcare providers, it did not independently predict satisfaction. Conclusion These findings underscore the importance of healthcare provider and social supportin increasing satisfaction with medical services among medically underserved populations.Developing tailored healthcare programs and specialized healthcare provider training are essential strategies to improve healthcare access and outcomes for these vulnerable groups.

Background: Primary cutaneous CD30+ lymphoproliferative disorders (pcCD30-LPDs) are a diseases with various clinical and prognostic characteristics. Objective: Increasing our knowledge of the clinical characteristics of pcCD30-LPDs and identifying potential prognostic variables in an Asian population. Methods: Clinicopathological features and survival data of pcCD30-LPD cases obtained from 22 hospitals in South Korea were examined. Results: A total of 413 cases of pcCD30-LPDs (lymphomatoid papulosis [LYP], n=237; primary cutaneous anaplastic large cell lymphoma [C-ALCL], n=176) were included. Ninety percent of LYP patients and roughly 50% of C-ALCL patients presented with multiple skin lesions. Both LYP and C-ALCL affected the lower limbs most frequently. Multiplicity and advanced T stage of LYP lesions were associated with a chronic course longer than 6 months. Clinical morphology with patch lesions and elevated serum lactate dehydrogenase were significantly associated with LPDs during follow-up in LYP patients. Extracutaneous involvement of C-ALCL occurred in 13.2% of patients. Lesions larger than 5 cm and increased serum lactate dehydrogenase were associated with a poor prognosis in C-ALCL. The survival of patients with C-ALCL was unaffected by the anatomical locations of skin lesions or other pathological factors. Conclusion The multiplicity or size of skin lesions was associated with a chronic course of LYP and survival among patients with C-ALCL.

Background/Aims: Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by fat accumulation in the liver. MASLD encompasses both steatosis and MASH. Since MASH can lead to cirrhosis and liver cancer, steatosis and MASH must be distinguished during patient treatment. Here, we investigate the genomes, epigenomes, and transcriptomes of MASLD patients to identify signature gene set for more accurate tracking of MASLD progression. Methods: Biopsy-tissue and blood samples from patients with 134 MASLD, comprising 60 steatosis and 74 MASH patients were performed omics analysis. SVM learning algorithm were used to calculate most predictive features. Linear regression was applied to find signature gene set that distinguish the stage of MASLD and to validate their application into independent cohort of MASLD. Results: After performing WGS, WES, WGBS, and total RNA-seq on 134 biopsy samples from confirmed MASLD patients, we provided 1,955 MASLD-associated features, out of 3,176 somatic variant callings, 58 DMRs, and 1,393 DEGs that track MASLD progression. Then, we used a SVM learning algorithm to analyze the data and select the most predictive features. Using linear regression, we identified a signature gene set capable of differentiating the various stages of MASLD and verified it in different independent cohorts of MASLD and a liver cancer cohort. Conclusions We identified a signature gene set (i.e., CAPG, HYAL3, WIPI1, TREM2, SPP1, and RNASE6) with strong potential as a panel of diagnostic genes of MASLD-associated disease.