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 To develop an innovative recognition algorithm that aids physicians in the identification of pulmonary nodules. Methods Patients with pulmonary nodules who underwent thoracoscopic surgery at the Department of Thoracic Surgery, Affiliated Drum Tower Hospital of Nanjing University Medical School in December 2023, were enrolled in the study. Chest surface exploration data were collected at a rate of 60 frames per second and a resolution of 1 920×1 080. Frame images were saved at regular intervals for subsequent block processing. An algorithm database for lung nodule recognition was developed using the collected data. Results A total of 16 patients were enrolled, including 9 males and 7 females, with an average age of (54.9±14.9) years. In the optimized multi-topology convolutional network model, the test results demonstrated an accuracy rate of 94.39% for recognition tasks. Furthermore, the integration of micro-variation amplification technology into the convolutional network model enhanced the accuracy of lung nodule identification to 96.90%. A comprehensive evaluation of the performance of these two models yielded an overall recognition accuracy of 95.59%. Based on these findings, we conclude that the proposed network model is well-suited for the task of lung nodule recognition, with the convolutional network incorporating micro-variation amplification technology exhibiting superior accuracy. Conclusion Compared to traditional methods, our proposed technique significantly enhances the accuracy of lung nodule identification and localization, aiding surgeons in locating lung nodules during thoracoscopic surgery.

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: Patients who report to be difficult to anesthetize for dental procedures are commonly encountered.Determining their frequency and shared characteristics could improve understanding of pain management failures. Methods: Categorical and continuous variables of 24 demographic, medical history, and dental history variables were compared in a deidentified cross-sectional study using electronic health records (EHR) of patients at the University of Iowa College of Dentistry. Individuals who self-reported to be difficult to anesthetize in their dental health history form were compared to those who reported no complications with local anesthesia. Descriptive, univariate regression, and multivariable regression statistical analyses were completed on the demographic, medical history, and dental history EHR variables. Results: A total of 12,400 deidentified patient records met the inclusion criteria with a 11.4% (n = 1,411) prevalence of difficult to anesthetize self-reports. Eight categorical variables were found to have statistically significant (95% confidence interval [CI]) adjusted odds ratios (AOR) in the multivariable regression of difficult to anesthetize reporting patients: female gender (AOR = 1.61, 95% CI: 1.32-1.96, P < 0.001), dental fear (AOR = 3.60, 95% CI: 3.01-4.31, P < 0.001), mental health disorders (AOR = 1.21, 95% CI: 1.00-1.46, P < 0.045), problems with general anesthesia (AOR = 1.46, 95% CI: 1.11-1.89, P = 0.005), neurologicalerve disorders (AOR = 1.30, 95% CI: 1.05-1.60, P = 0.015), temporomandibular joint clicking/popping (AOR = 1.31, 95% CI: 1.08-1.60, P = 0.006), needle anxiety (AOR = 29.03, 95% CI: 23.80-35.52, P < 0.001), and history of root canal treatment (AOR 0.82, 95% CI: 0.68-0.99, P = 0.035). Conclusion A clinically relevant percentage of patients self-reported being difficult to anesthetize for dental procedures. The relationship between local anesthesia inadequacies and variables such as female gender, dental fear, mental health, and neurological disorders requires further investigation. The use of evidence-based local anesthesia approaches and communication practices is suggested to minimize pain experienced and subsequent fear of dental care.

Background: Patients who report to be difficult to anesthetize for dental procedures are commonly encountered.Determining their frequency and shared characteristics could improve understanding of pain management failures. Methods: Categorical and continuous variables of 24 demographic, medical history, and dental history variables were compared in a deidentified cross-sectional study using electronic health records (EHR) of patients at the University of Iowa College of Dentistry. Individuals who self-reported to be difficult to anesthetize in their dental health history form were compared to those who reported no complications with local anesthesia. Descriptive, univariate regression, and multivariable regression statistical analyses were completed on the demographic, medical history, and dental history EHR variables. Results: A total of 12,400 deidentified patient records met the inclusion criteria with a 11.4% (n = 1,411) prevalence of difficult to anesthetize self-reports. Eight categorical variables were found to have statistically significant (95% confidence interval [CI]) adjusted odds ratios (AOR) in the multivariable regression of difficult to anesthetize reporting patients: female gender (AOR = 1.61, 95% CI: 1.32-1.96, P < 0.001), dental fear (AOR = 3.60, 95% CI: 3.01-4.31, P < 0.001), mental health disorders (AOR = 1.21, 95% CI: 1.00-1.46, P < 0.045), problems with general anesthesia (AOR = 1.46, 95% CI: 1.11-1.89, P = 0.005), neurologicalerve disorders (AOR = 1.30, 95% CI: 1.05-1.60, P = 0.015), temporomandibular joint clicking/popping (AOR = 1.31, 95% CI: 1.08-1.60, P = 0.006), needle anxiety (AOR = 29.03, 95% CI: 23.80-35.52, P < 0.001), and history of root canal treatment (AOR 0.82, 95% CI: 0.68-0.99, P = 0.035). Conclusion A clinically relevant percentage of patients self-reported being difficult to anesthetize for dental procedures. The relationship between local anesthesia inadequacies and variables such as female gender, dental fear, mental health, and neurological disorders requires further investigation. The use of evidence-based local anesthesia approaches and communication practices is suggested to minimize pain experienced and subsequent fear of dental care.

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: Patients who report to be difficult to anesthetize for dental procedures are commonly encountered.Determining their frequency and shared characteristics could improve understanding of pain management failures. Methods: Categorical and continuous variables of 24 demographic, medical history, and dental history variables were compared in a deidentified cross-sectional study using electronic health records (EHR) of patients at the University of Iowa College of Dentistry. Individuals who self-reported to be difficult to anesthetize in their dental health history form were compared to those who reported no complications with local anesthesia. Descriptive, univariate regression, and multivariable regression statistical analyses were completed on the demographic, medical history, and dental history EHR variables. Results: A total of 12,400 deidentified patient records met the inclusion criteria with a 11.4% (n = 1,411) prevalence of difficult to anesthetize self-reports. Eight categorical variables were found to have statistically significant (95% confidence interval [CI]) adjusted odds ratios (AOR) in the multivariable regression of difficult to anesthetize reporting patients: female gender (AOR = 1.61, 95% CI: 1.32-1.96, P < 0.001), dental fear (AOR = 3.60, 95% CI: 3.01-4.31, P < 0.001), mental health disorders (AOR = 1.21, 95% CI: 1.00-1.46, P < 0.045), problems with general anesthesia (AOR = 1.46, 95% CI: 1.11-1.89, P = 0.005), neurologicalerve disorders (AOR = 1.30, 95% CI: 1.05-1.60, P = 0.015), temporomandibular joint clicking/popping (AOR = 1.31, 95% CI: 1.08-1.60, P = 0.006), needle anxiety (AOR = 29.03, 95% CI: 23.80-35.52, P < 0.001), and history of root canal treatment (AOR 0.82, 95% CI: 0.68-0.99, P = 0.035). Conclusion A clinically relevant percentage of patients self-reported being difficult to anesthetize for dental procedures. The relationship between local anesthesia inadequacies and variables such as female gender, dental fear, mental health, and neurological disorders requires further investigation. The use of evidence-based local anesthesia approaches and communication practices is suggested to minimize pain experienced and subsequent fear of dental care.

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.

[摘 要] 目的：为解决野生型B细胞成熟抗原（BCMA）被γ分泌酶切割导致表达不稳定的问题，构建抵抗γ分泌酶切割的BCMA突变体并构建靶细胞，用于评价BCMA CAR-T细胞的杀伤功能。方法：将野生型BCMA的穿膜域替换为人CD8α穿膜域，构建抵抗γ分泌酶切割的BCMA突变体（BCMA-CD8α TM），构建过表达该突变体的U266（U266BCMA Mut）、K562（K562BCMA Mut）、SKOV3（SKOV3BCMA Mut）和CHO（CHOBCMA Mut）细胞；构建装载NFAT-EGFP报告基因的BCMA CAR Jurkat细胞（BCMA-CAR-Jurkat-Reporter）与U266BCMA Mut细胞共培养，采用FCM检测该细胞中EGFP表达水平以指示NFAT激活水平，荧光素酶法检测BCMA CAR-T细胞对Luciferase标记的K562BCMA Mut细胞的杀伤作用，实时无标记动态细胞分析技术（RTCA）检测BCMA CAR-T细胞对SKOV3BCMA Mut和CHOBCMA Mut细胞的杀伤作用。结果：应用γ分泌酶抑制剂LY411575抑制γ分泌酶活性，显著增强野生型U266细胞表面BCMA表达水平，平均荧光强度上调10倍以上；但撤除抑制剂后BCMA表达水平逐渐降低（P<0.01）；BCMA-CD8α TM突变体可抵抗γ分泌酶的切割作用，在U266细胞表面稳定表达（P>0.05）；U266细胞及过表达BCMA-CD8α TM的U266细胞与BCMA-CAR-Jurkat-Reporter细胞共培养后都可激活Reporter系统、增强EGFP表达，但该效应在BCMA-CD8α TM过表达的U266细胞中更显著（P<0.01）；BCMA-CD8α TM在BCMA表达阴性的K562、SKOV3和CHO 3种靶细胞中成功过表达，且在LY411575处理下该突变体的表达水平仅有小幅度升高；荧光素酶法检测结果显示，不同效靶比下，BCMA CAR-T细胞均可特异、高效杀伤过表达BCMA-CD8α TM的K562细胞；RTCA结果显示，不同效靶比下，BCMA CAR-T细胞均可有效识别、杀伤过表达BCMA-CD8α TM的SKOV3和CHO细胞，但同等效靶比下的Mock-T细胞无此效应。结论：本实验构建的BCMA-CD8α TM突变体能够抵抗γ分泌酶的切割，在多种靶细胞表面稳定表达，为评价BCMA CAR-T细胞体外杀伤的有效性和特异性提供多种检测手段。