Debates persist regarding the efficacy and safety of azvudine, particularly its real-world outcomes. This study involved patients aged ≥60 years who were admitted to 25 hospitals in mainland China with confirmed SARS-CoV-2 infection between December 1, 2022, and February 28, 2023. Efficacy outcomes were all-cause mortality during hospitalization, the proportion of patients discharged with recovery, time to nucleic acid-negative conversion (T _NANC), time to symptom improvement (T _SI), and time of hospital stay (T _HS). Safety was also assessed. Among the 5884 participants identified, 1999 received azvudine, and 1999 matched controls were included after exclusion and propensity score matching. Azvudine recipients exhibited lower all-cause mortality compared with controls in the overall population (13.3% vs. 17.1%, RR, 0.78; 95% CI, 0.67-0.90; P = 0.001) and in the severe subgroup (25.7% vs. 33.7%; RR, 0.76; 95% CI, 0.66-0.88; P < 0.001). A higher proportion of patients discharged with recovery, and a shorter T _NANC were associated with azvudine recipients, especially in the severe subgroup. The incidence of adverse events in azvudine recipients was comparable to that in the control group (2.3% vs. 1.7%, P = 0.170). In conclusion, azvudine showed efficacy and safety in older patients hospitalized with COVID-19 during the SARS-CoV-2 omicron wave in China.

Objective To investigate the effect of pneumoperitoneum pressure during robotic-assisted kidney transplantation (RAKT) on the function of the transplant kidney. Methods The data of 243 kidney transplant recipients were retrospectively analyzed and divided into open kidney transplantation (OKT) group (n=105) and RAKT group (n=138). The RAKT group was further divided into 13 mmHg group (n=67) and 7 mmHg group (n=71) based on pneumoperitoneum pressure. The donor information, recipient's preoperative general data, intraoperative data, and postoperative recovery of the three groups were compared. In the RAKT group, the renal artery, segmental artery, interlobar artery, and venous flow velocity of the transplant kidney were measured using laparoscopic ultrasound. Results There was a statistically significant difference in donor types among the groups (P<0.05), while other donor information and recipient's preoperative general data showed no statistically significant differences (all P>0.05). There were no statistically significant differences in serum creatinine and complications at 30 days and 1 year postoperatively among the groups (all P>0.05). The OKT group and 7 mmHg group had more intraoperative urine output than the 13 mmHg group. Both RAKT groups had less intraoperative blood loss and shorter hospital stays than the OKT group, and longer operation times than the OKT group (all P<0.05). There were no statistically significant differences in operation time, intraoperative blood loss, and hospital stay between the two RAKT groups (all P>0.05). The vascular flow velocity of the transplant kidney decreased at 13 mmHg compared to 7 mmHg pneumoperitoneum pressure, but the differences were not statistically significant (all P>0.05). Conclusions Controllable pneumoperitoneum pressure has a limited impact on the vascular flow velocity of the transplanted kidney. RAKT is a safe and effective surgical method under appropriate pneumoperitoneum pressure, and choosing a lower pneumoperitoneum pressure is more conducive to the early recovery of renal function postoperatively.

Objective:To analyze risk factors for immune checkpoint inhibitor-related pneumonitis (CIP) in non-small cell lung cancer (NSCLC) patients based on clinical and radiological characteristics, and to develop and validate a nomogram model for predicting the risk of CIP.Methods:A retrospective case-controlled study was conducted. The clinical data of 159 patients diagnosed with NSCLC in Shanxi Province Cancer Hospital between January 2020 and December 2023 who received immune checkpoint inhibitor (ICI) therapy were retrospectively analyzed. Based on the development of CIP after immunotherapy, the patients were divided into the CIP group (30 cases) and the control group (129 cases). The clinical data of NSCLC patients, hematological indicators and the data of imaging characteristics before their first ICI treatment were collected. Quantitative assessments were performed on pretreatment chest CT images, including lung total tumor volume, number of involved lung segments, and pulmonary infection index. Logistic regression analysis was used to screen out the factors influencing the development of CIP. R 4.3.0 statistical software was used to construct a nomogram model for predicting CIP based on the statistically significant risk factors identified in the multivariate logistic regression analysis. The predictive performance of the model was evaluated by using receiver operating characteristic (ROC) curves and the area under the curve (AUC). Calibration curves and decision curve analysis (DCA) were employed to assess the model's consistency and clinical benefit.Results:There were statistically significant differences in the proportions of patients with a history of chest radiotherapy and those receiving different immunotherapy regimens between the control group and the CIP group (both P < 0.001). The difference in the lactate dehydrogenase (LDH) [ M ( IQR)] between the both groups was statistically significant [211.00 U/L (57.00 U/L) vs. 276.00 U/L (136.00 U/L), Z = -3.41, P < 0.001]; additionally, the difference in lung status score between the 2 groups was statistically significant ( P < 0.001). Multivariate logistic regression analysis revealed that a history of chest radiotherapy (with vs. without: OR = 4.200, 95% CI: 1.466-12.036), the combination of immunotherapy (monotherapy vs. the combined therapy: OR = 0.106, 95% CI: 0.022-0.509), LDH ≥ 255.5 U/L (< 255.5 U/L vs. ≥ 255.5 U/L: OR = 0.988, 95% CI: 0.981-0.995), and severe lung status score(mild vs. moderate vs. severe: OR = 0.187, 95% CI: 0.059-0.593) were independent risk factors for CIP development in NSCLC patients after immunotherapy (all P < 0.05). A nomogram model for predicting CIP occurrence was constructed based on chest radiotherapy history, immunotherapy regimen, LDH, and lung status score. ROC curve analysis showed the AUC was 0.878 (95% CI: 0.813-0.942). The calibration curve demonstrated the good consistency between the predicted risk probability of CIP and the observed outcomes; DCA indicated that the model had favorable clinical benefits. Conclusions:The constructed nomogram prediction model shows a good predictive performance.

Objective:To evaluate the value of ultrasound radiomics combined with machine learning for early diagnosis of seronegative Hashimoto’s thyroiditis (SN-HT) .Methods:This retrospective study included 164 patients from Liaoning Provincial People’s Hospital , Lixin County People’s Hospital, Linghai Dalinghe Hospital, Fengcheng Phoenix Hospital, who underwent thyroidectomy for solitary nodules with normal thyroid function between Nov. 2016 and Jan. 2024. Postoperative pathology confirmed Hashimoto’s thyroiditis (HT) in some cases, who were further categorized into antibody-positive and antibody-negative groups based on serum antibody status. Patients without Hashimoto’s thyroiditis served as the control group. A total of 298 ultrasound images were analyzed. Radiomics features were extracted from hypoechoic non-nodular areas within 0.5 cm surrounding the tumor. Two senior pathologists and two senior ultrasound physicians independently assessed lymphocytic infiltration, eosinophilic changes of follicular epithelium, and the proportion of hypoechoic areas in pathology and ultrasound images, respectively. A machine learning model, CCH-NET, was developed using linear regression and t-distributed stochastic neighbor embedding (t-SNE) techniques. The dataset was divided into a training set (80%) and a validation set (20%) to compare the diagnostic accuracy of CCH-NET with that of senior ultrasound physicians. Results:In internal validation, CCH-NET achieved a diagnostic accuracy of 88.89% for both antibody-positive and antibody-negative groups, significantly higher than the 66.67% accuracy of senior ultrasound physicians ( P<0.01). In external validation, CCH-NET achieved 75.00% and 66.67% accuracy for the two groups, compared to 50.00% by senior ultrasound physicians. For the control group, both methods achieved 93.33% accuracy. The AUC of CCH-NET was 0.848, outperforming senior ultrasound physicians (0.681) ,demonstrating superior diagnostic performance. Conclusion:The radiomics-based CCH-NET model, using non-nodular hypoechoic areas as a specific indicator, can accurately identify early SN-HT in euthyroid patients. It significantly outperforms senior ultrasound physicians, improving diagnostic accuracy and reducing missed diagnoses.

Objective:To analyze risk factors for immune checkpoint inhibitor-related pneumonitis (CIP) in non-small cell lung cancer (NSCLC) patients based on clinical and radiological characteristics, and to develop and validate a nomogram model for predicting the risk of CIP.Methods:A retrospective case-controlled study was conducted. The clinical data of 159 patients diagnosed with NSCLC in Shanxi Province Cancer Hospital between January 2020 and December 2023 who received immune checkpoint inhibitor (ICI) therapy were retrospectively analyzed. Based on the development of CIP after immunotherapy, the patients were divided into the CIP group (30 cases) and the control group (129 cases). The clinical data of NSCLC patients, hematological indicators and the data of imaging characteristics before their first ICI treatment were collected. Quantitative assessments were performed on pretreatment chest CT images, including lung total tumor volume, number of involved lung segments, and pulmonary infection index. Logistic regression analysis was used to screen out the factors influencing the development of CIP. R 4.3.0 statistical software was used to construct a nomogram model for predicting CIP based on the statistically significant risk factors identified in the multivariate logistic regression analysis. The predictive performance of the model was evaluated by using receiver operating characteristic (ROC) curves and the area under the curve (AUC). Calibration curves and decision curve analysis (DCA) were employed to assess the model's consistency and clinical benefit.Results:There were statistically significant differences in the proportions of patients with a history of chest radiotherapy and those receiving different immunotherapy regimens between the control group and the CIP group (both P < 0.001). The difference in the lactate dehydrogenase (LDH) [ M ( IQR)] between the both groups was statistically significant [211.00 U/L (57.00 U/L) vs. 276.00 U/L (136.00 U/L), Z = -3.41, P < 0.001]; additionally, the difference in lung status score between the 2 groups was statistically significant ( P < 0.001). Multivariate logistic regression analysis revealed that a history of chest radiotherapy (with vs. without: OR = 4.200, 95% CI: 1.466-12.036), the combination of immunotherapy (monotherapy vs. the combined therapy: OR = 0.106, 95% CI: 0.022-0.509), LDH ≥ 255.5 U/L (< 255.5 U/L vs. ≥ 255.5 U/L: OR = 0.988, 95% CI: 0.981-0.995), and severe lung status score(mild vs. moderate vs. severe: OR = 0.187, 95% CI: 0.059-0.593) were independent risk factors for CIP development in NSCLC patients after immunotherapy (all P < 0.05). A nomogram model for predicting CIP occurrence was constructed based on chest radiotherapy history, immunotherapy regimen, LDH, and lung status score. ROC curve analysis showed the AUC was 0.878 (95% CI: 0.813-0.942). The calibration curve demonstrated the good consistency between the predicted risk probability of CIP and the observed outcomes; DCA indicated that the model had favorable clinical benefits. Conclusions:The constructed nomogram prediction model shows a good predictive performance.

Objective:To evaluate the value of ultrasound radiomics combined with machine learning for early diagnosis of seronegative Hashimoto’s thyroiditis (SN-HT) .Methods:This retrospective study included 164 patients from Liaoning Provincial People’s Hospital , Lixin County People’s Hospital, Linghai Dalinghe Hospital, Fengcheng Phoenix Hospital, who underwent thyroidectomy for solitary nodules with normal thyroid function between Nov. 2016 and Jan. 2024. Postoperative pathology confirmed Hashimoto’s thyroiditis (HT) in some cases, who were further categorized into antibody-positive and antibody-negative groups based on serum antibody status. Patients without Hashimoto’s thyroiditis served as the control group. A total of 298 ultrasound images were analyzed. Radiomics features were extracted from hypoechoic non-nodular areas within 0.5 cm surrounding the tumor. Two senior pathologists and two senior ultrasound physicians independently assessed lymphocytic infiltration, eosinophilic changes of follicular epithelium, and the proportion of hypoechoic areas in pathology and ultrasound images, respectively. A machine learning model, CCH-NET, was developed using linear regression and t-distributed stochastic neighbor embedding (t-SNE) techniques. The dataset was divided into a training set (80%) and a validation set (20%) to compare the diagnostic accuracy of CCH-NET with that of senior ultrasound physicians. Results:In internal validation, CCH-NET achieved a diagnostic accuracy of 88.89% for both antibody-positive and antibody-negative groups, significantly higher than the 66.67% accuracy of senior ultrasound physicians ( P<0.01). In external validation, CCH-NET achieved 75.00% and 66.67% accuracy for the two groups, compared to 50.00% by senior ultrasound physicians. For the control group, both methods achieved 93.33% accuracy. The AUC of CCH-NET was 0.848, outperforming senior ultrasound physicians (0.681) ,demonstrating superior diagnostic performance. Conclusion:The radiomics-based CCH-NET model, using non-nodular hypoechoic areas as a specific indicator, can accurately identify early SN-HT in euthyroid patients. It significantly outperforms senior ultrasound physicians, improving diagnostic accuracy and reducing missed diagnoses.

Objective To explore the learning curve of robotic-assisted kidney transplantation(RAKT).Methods The clinical data of 96 consecutive RAKT patients performed by the same surgical team were retrospectively analyzed.The arterial anastomosis time,venous anastomosis time,ureteral anastomosis time,hospital stay,and blood loss were selected as evaluation indicators.The learning curve of RAKT was analyzed using the cumulative sum(CUSUM),and the curve was divided into the learning improvement stage and the proficient mastery stage according to the learning curve.The learning curve was verified by comparing the general data and surgical data of patients in different learning stages,and the clinical efficacy of each stage was analyzed.Results The optimal fitting equation of the learning curve reached its peak at the 33rd case,which was the minimum number of surgeries required to master RAKT.There was no statistically significant difference in age,gender,dialysis type,previous abdominal surgery history,number of donor renal arteries,and preoperative serum creatinine between the learning improvement group and the proficient mastery group(all P>0.05).Compared with the learning improvement stage,the body mass index(BMI)was higher,and the number of right donor kidney was increased compared to the left donor kidney in the proficient mastery stage(both P<0.05).There were no significant differences in arterial anastomosis time,ureteral anastomosis time,postoperative serum creatinine,and complications between the two groups(all P>0.05).The iliac vessel dissection time,warm ischemia time,venous anastomosis time,blood loss,and hospital stay in the proficient mastery stage were superior to those in the learning improvement stage,with statistically significant differences(all P<0.05).Conclusions RAKT requires at least 33 cases to cross the learning curve.There is no difference in complications and recovery of transplant renal function between the learning improvement stage and the proficient mastery stage.

In the treatment process of proton radiation therapy,the patient needs to be positioned and immobilized before being moved into the treatment position.In this study,the patient was primarily positioned using the 6R robotic treatment couch as the patient support system(PSS).A simplified three-dimensional model of the treatment room was developed based on the relative motion within the treatment room.The forward and inverse kinematics of the 6R robotic treatment couch were analyzed using an improved Denavit-Hartenberg(D-H)representation.A collision interference model was created based on the actual treatment process.The motion path of the treatment couch was planned and simulated in MATLAB using an improved artificial potential field method for obstacle avoidance.The results indicate that the robotic treatment couch can smoothly navigate around obstacles to reach the target point,satisfying the positioning requirements for proton therapy.

With the improved understanding of driver mutations in non-small cell lung cancer (NSCLC), expanding the targeted therapeutic options improved the survival and safety. However, responses to these agents are commonly temporary and incomplete. Moreover, even patients with the same oncogenic driver gene can respond diversely to the same agent. Furthermore, the therapeutic role of immune-checkpoint inhibitors (ICIs) in oncogene-driven NSCLC remains unclear. Therefore, this review aimed to classify the management of NSCLC with driver mutations based on the gene subtype, concomitant mutation, and dynamic alternation. Then, we provide an overview of the resistant mechanism of target therapy occurring in targeted alternations ("target-dependent resistance") and in the parallel and downstream pathways ("target-independent resistance"). Thirdly, we discuss the effectiveness of ICIs for NSCLC with driver mutations and the combined therapeutic approaches that might reverse the immunosuppressive tumor immune microenvironment. Finally, we listed the emerging treatment strategies for the new oncogenic alternations, and proposed the perspective of NSCLC with driver mutations. This review will guide clinicians to design tailored treatments for NSCLC with driver mutations.
Humans ; Carcinoma, Non-Small-Cell Lung/genetics* ; Lung Neoplasms/genetics* ; Mutation ; Tumor Microenvironment/genetics*

Objective To establish a PCR-based capillary electrophoresis(PCR/CE)to detect Survival Motor Neuron 1(SMN1)and Survival Motor Neuron 2(SMN2)genes and to evaluate its performance.Methods PCR/CE and Multiplex Ligation-dependent Probe Amplification(MLPA)for SMA gene diagnosis were used to blindly test the samples in sync.The performance of PCR/CE was assessed using MLPA results as the standard.Results A total of 336 samples were included in this study,consisting of 50 homozygous deletion types(14.9%),65 heterozygous deletion types(19.3%),and 221 non-deletion types(65.8%).The results of PCR/CE for detect-ing SMN1 and SMN2 copy numbers(0,1,2,3,≥4)were in complete agreement with the results of the MLPA.Conclusions PCR/CE for gene testing related to SMA could accurately detect copy numbers of exon 7 and exon 8 of the SMN1 and SMN2 genes(0,1,2,3,≥4).