Amrubicin (AMR) regimens have shown efficacy as second‐line treatment in patients with small cell lung cancer (SCLC); however, adverse events such as febrile neutropenia (FN) sometimes preclude their use. Further, the safety and efficacy of AMR with primary prophylactic pegfilgrastim (P‐PEG) have not been sufficiently evaluated. In this study, we evaluated the safety and efficacy of AMR with or without P‐PEG as second‐line chemotherapy for SCLC.n = 60) were divided into P‐PEG (n = 21) and non‐P‐PEG groups, and their clinical outcomes were evaluated.We retrospectively reviewed patients with SCLC who received AMR as second‐line chemotherapy at Shizuoka Cancer Center, between December 2014 and November 2021. Based on presence/absence of P‐PEG in their regimen, patients (p = 0.02) and AMR dose reduction because of adverse events  were lower in the P‐PEG group than in the non‐P‐PEG group. The objective response rates were 52.4% and 30.8%, and median progression‐free and overall survival were 4.7 and 3.0 months, and 9.6 and 6.8 months, in the P‐PEG and non‐P‐PEG groups, respectively.Median of AMR treatment cycles was five (range: 1–39 cycles) in P‐PEG group and four (range: 1–15 cycles) in non‐P‐PEG group. The incidence of FN  in combination with primary prophylactic pegfilgrastim (P‐PEG) as second‐line chemotherapy for small cell lung cancer (SCLC) reduced the incidence of febrile neutropenia and was associated with favorable tumor responses. Survival outcomes such as progression‐free and overall survival tended to be better in AMR with P‐PEG compared to without P‐PEG. P‐PEG should be considered for patients treated with AMR for SCLC, especially those with refractory relapsed SCLC. Lung cancer is one of the most common malignancies and the leading cause of death worldwide.AMR therapy has demonstrated favorable tumor responses and survival outcomes in patients with relapsed SCLC; however, this agent can cause hematological toxicity, particularly febrile neutropenia (FN), with an occurrence of 10%–27% reported in clinical trials.2 as second‐line chemotherapy at Shizuoka Cancer Center between December 2014 and November 2021. The patients were grouped and evaluated separately as those either receiving or not receiving P‐PEG. The date for data cutoff was March 31, 2022. This study was approved by the Institutional Review Board of Shizuoka Cancer Center and conducted in accordance with the principles stated in the Ethical Guidelines for Medical and Biological Research Involving Human Subjects.We retrospectively collected data from patients with SCLC who received AMR at a dose of 40 mg/m2 intravenously on days 1–3. The decision to administer P‐PEG was based on consultation between the attending physician and the patient. In the P‐PEG group, primary prophylactic PEG was administered subcutaneously after day 4 of AMR therapy. In the non‐P‐PEG group, G‐CSF was administered in cases where FN and/or grade 4 neutropenia developed. For patients in the non‐P‐PEG group who had FN or severe neutropenia in the previous course, prophylactic PEG was administered on day 4 of the second and subsequent courses, which was defined as secondary prophylactic PEG. The attending physician also decided whether to reduce the AMR dose depending on the adverse events that developed in the previous cycle. When dose reduction was performed, the dose was reduced stepwise to 35 mg/m2, and then to 30 mg/m2. Treatment was continued until disease progression was observed, unacceptable toxicity, and/or patient request.All patients received AMR every 3 weeks at a dose of 40 mg/mClinical data such as age, sex, Eastern Cooperative Oncology Group performance status (PS), previous regimen, episode of FN prior to chemotherapy, and history of thoracic radiation therapy were collected from patient medical records. Both refractory and sensitive relapse were included in the study; refractory relapse was defined as no response to first‐line chemotherapy, disease progression while on first‐line chemotherapy, or disease progression within 90 days of completing first‐line chemotherapy after confirming a complete or partial response. Adverse events were graded using the Common Terminology Criteria for Adverse Events (version 5.0). Tumor response was assessed according to the Response Evaluation Criteria for Solid Tumors (version 1.1).p‐value less than 0.05 was considered to indicate statistical significance. All analyses were performed using EZR version 1.55 .Fisher's exact test was used to compare categorical data as follows: patient characteristics, AMR treatment delivery, adverse events, and tumor response. Survival estimation was performed using the Kaplan–Meier method, and subgroups were compared using the log‐rank test. We calculated the hazard ratio for PFS and OS using the Cox proportional hazards model. All statistical tests were two‐sided, and a A total of 60 patients were enrolled in the study: 21 in the P‐PEG group and 39 in the non‐P‐PEG group. The patient characteristics are summarized in Table p = 0.10). AMR dose reduction tended to be less frequent in the P‐PEG group compared with that in the non‐P‐PEG group . The reasons for dose reduction were grade 4 neutropenia (N = 1) in the P‐PEG group, and FN (N = 7) and grade 4 neutropenia (N = 3) in the non‐P‐PEG group. Treatment was discontinued in one patient (grade 3 pneumonia) in the P‐PEG group and three patients (grade 3 FN) in the non‐P‐PEG group, because of toxicity. Among the 32 patients who received more than two cycles of AMR in the non‐P‐PEG group, seven received secondary prophylactic PEG.The administration regimen of AMR is summarized in Table p < 0.01), leukopenia , and FN  was significantly lower in the P‐PEG group than in the non‐P‐PEG group. The incidence of grade 3 or higher adverse events was significantly decreased in the P‐PEG group compared with that in the non‐P‐PEG group.Grade 3 or higher adverse events in the patients are summarized in Table p = 0.16). The median PFS was 4.7 months  in the P‐PEG group and 3.0 months (95% CI: 2.0–3.7 months) in the non‐P‐PEG group    was 52.4% and 30.8% in the P‐PEG and non‐P‐PEG groups, respectively (Table p = 0.29). The median OS was 9.2 months (95% CI: 5.1–14.8 months) in the P‐PEG group and 6.8 months (95% CI: 4.6–12.3 months) in the non‐P‐PEG group .In a subset analysis of 53 patients with refractory relapsed SCLC (18 in the P‐PEG group and 35 in the non‐P‐PEG group), the ORR was 44.4% and 26.6% in the P‐PEG and non‐P‐PEG groups, respectively. The median PFS values were 3.5 months (95% CI: 2.0–5.6 months) in the P‐PEG group and 3.0 months (95% CI: 2.0–3.6 months) in the non‐P‐PEG group  received AMR therapy with P‐PEG. Although there were no significant differences in patient characteristics between the groups, the incidence of FN and AMR dose reduction because of adverse events was lower in the P‐PEG group than in the non‐P‐PEG group. In a previous prospective study, the incidence of FN and AMR dose reduction in patients with SCLC was 27% and 38%, respectively, similar to that in patients in the non‐P‐PEG group in the present study.In a recent retrospective study, the real‐world incidence of FN in patients with thoracic malignancies, including SCLC treated with AMR, was 30%, and the OS of patients with FN was significantly shorter than that of patients without.However, this study had several limitations. First, it was a single‐center retrospective study with a small sample size. Second, the study included both refractory and sensitive relapses, which allowed for heterogeneity in terms of patient backgrounds. Studies on larger number of patients are needed to confirm the superior survival conferred by AMR with P‐PEG therapy for refractory and sensitive relapsed SCLC. Third, we could not assess the quality of life of patients and cost effectiveness of therapy. The reduction in severe hematologic toxicity, such as FN, could be related to improvements in the quality of life of patients and cost effectiveness, which should be evaluated in the future. In order to resolve the above three limitations, we will continue to accumulate and examine further cases.In conclusion, AMR in combination with P‐PEG as second‐line chemotherapy for patients with SCLC reduced the incidence of FN without AMR dose adjustment and was associated with favorable tumor responses and survival outcomes compared to those observed with AMR monotherapy. P‐PEG should be considered for patients treated with AMR for SCLC, especially those with refractory relapsed SCLC.Conceptualization: Motoki Sekikawa and Haruyasu Murakami. Methodology: Motoki Sekikawa and Haruyasu Murakami. Project Administration: Haruyasu Murakami. Formal Analysis: Motoki Sekikawa. Writing—Original Draft: Motoki Sekikawa. Writing–Review & Editing: Meiko Morita, Kosei Doshita, Keita Miura, Hiroaki Kodama, Noboru Morikawa, Yuko Iida, Nobuaki Mamesaya, Haruki Kobayashi, Ryo Ko, Kazushige Wakuda, Akira Ono, Hirotsugu Kenmotsu, and Tateaki Naito. Supervision: Hirofumi Chiba and Toshiaki Takahashi. All authors have read and approved the final version of the manuscript.Motoki Sekikawa reports personal fees from Chugai pharma, outside the submitted work. Haruyasu Murakami reports grants from AstraZeneca, Chugai pharma, Takeda, Abbvie, Daiichi Sankyo, and IQvia and personal fees from AstraZeneca, Chugai pharma, Takeda, Abbvie, Daiichi Sankyo, MSD, Ono Pharmaceutical, Bristol‐Myers Squibb Japan, Pfizer, Novartis, Eli Lilly Japan, Taiho Pharmaceutical, Boehringer Ingelheim, Eisai, and Nippon Kayaku, outside the submitted work. Kosei Doshita reports personal fees from Eli Lilly Japan, AstraZeneca, and Chugai pharma, outside the submitted work. Keita Miura reports personal fees from Chugai pharma and Taiho Pharmaceutical, outside the submitted work. Hiroaki Kodama reports personal fees from Chugai pharma, AstraZeneca, and Boehringer Ingelheim, outside the submitted work. Nobuaki Mamesaya reports grants from Boehringer Ingelheim and personal fees from Chugai pharma, AstraZeneca, Boehringer Ingelheim, Taiho Pharmaceutical, MSD, and Ono Pharmaceutical, outside the submitted work. Haruki Kobayashi reports personal fees from Eli Lilly, Novartis, Taiho Pharmaceutical, Chugai pharma, AstraZeneca, Ono Pharmaceutical, and Bristol‐Myers Squibb Japan, outside the submitted work. Ryo Ko reports grants from MSD and AstraZeneca and personal fees from Taiho Pharmaceutical, Chugai Pharmaceutical, Eli Lilly, Boehringer Ingelheim, Pfizer, AstraZeneca, Ono Pharmaceutical, Daiichi Sankyo, and Takeda, MSD, outside the submitted work. Kazushige Wakuda reports grants from AstraZeneca, Chugai pharma, Novartis, Abbvie, AMGEN, Daiichi Sankyo, and Dizal Pharma and personal fees from Chugai pharma, AstraZeneca, Taiho Pharmaceutical, Boehringer Ingelheim, Eli Lilly, MSD, Ono Pharmaceutical, Daiichi Sankyo, Jassen Pharmaceutial, Takeda, and Nippon Kayaku, outside the submitted work. Akira Ono reports personal fees from Chugai pharma, AstraZeneca, and Ono Pharmaceutical, outside the submitted work. Hirotsugu Kenmotsu reports grants from Ono Pharmaceutical, Novartis, Eli Lilly, AstraZeneca, and Loxo Oncology and personal fees from AMGEN, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol‐Myers Squibb, Chugai pharma, Daiichi Sankyo, Eli Lilly, Kyowa Hakko Kirin, Merck, MSD, Novartis, Ono Pharmaceutical, Pfizer, Taiho Pharmaceutical, and Takeda, outside the submitted work. Tateaki Naito reports grants from Otsuka Pharmaceutical and personal fees from Ono Pharmaceutical and HELSINN SA, outside the submitted work. Hirofumi Chiba reports personal fees from Boehringer Ingelheim, outside the submitted work. Toshiaki Takahashi reports grants from AstraZeneca, Chugai Pharma, Eli Lilly Japan, Ono Pharmaceutical, MSD, Pfizer, AMGEN, Boehringer Ingelheim, and Merck Biopharma and personal fees from AstraZeneca, Chugai Pharma, Eli Lilly Japan, Ono Pharmaceutical, MSD, Pfizer, Boehringer Ingelheim, Roche Diagnostics, and Takeda, Yakult, outside the submitted work. All other authors have nothing to disclose.


The curative treatment for Stage I non‐small cell lung cancer (NSCLC) is surgical resection. Even for Stage I patients, the probability of recurrence after curative treatment is around 20%.In this retrospective study, we included 268 operated Stage I NSCLC patients between January 2008 and June 2018 to analyze the prognostic factors  that may affect relapse with three different methods, Cox proportional hazard (CoxPH), random survival forest (RSF), DeepSurv, and to compare the performance of these methods with Harrell's C‐index. The dataset was randomly split into two sets, training and test sets.In the training set, DeepSurv showed the best performance among the three models, the C‐index of the training set was 0.832, followed by RSF (0.675) and CoxPH (0.672). In the test set, RSF showed the best performance among the three models, followed by DeepSurv with 0.677 and CoxPH methods with 0.625.In conclusion, machine‐learning techniques can be useful in predicting recurrence for lung cancer and guide clinicians both in choosing the adjuvant treatment options and best follow‐up programs. More than 2 million new cases are seen every year.There are some literature applying machine learning to various types of cancer datasets have reported good results. Kim et al,This study aimed to analyze the prognostic factors that may affect relapse in patients with operated Stage I NSCLC with three different methods such as CoxPH, RSF, and DeepSurv, and compare the performance of these methods with Harrell's C‐index.22.1X1–X3), four (X1–X4), five (X1–X5), six (X1–X6), and seven (X1–X7) variables were built, respectively.In this retrospective study, 268 patients who were operated for NSCLC between January 2008 and June 2018 in the Department of Thoracic Surgery, Faculty of Medicine, Ankara University were included in the study. The study was approved by the Ethical Committee of the Faculty of Medicine, Ankara University . Written informed consent was obtained from all participants included in this study. Patients with Stage I pathological staging were included. Patients who received neoadjuvant therapy, had positive surgical margins, had a rare type of lung cancer, had synchronous–metachronous lung cancer, and had missing data were excluded from the study. Risk factors that may affect recurrence, such as pathological stage, histological type, number of sampled mediastinal lymph node stations, type of resection, and SUVmax of the lesion were recorded and included in the analysis. After the preprocessing step, the variables given in Table 2.2p < 0.05 were considered statistically significant. The primary aim of this study was to compare the classification performances of the DeepSurv, RSF, and CoxPH models in the NSCLC dataset. Statistical analyses were performed using Statistical Package for Social Sciences . The dataset was randomly split into two mutually exclusive data sets, training and test sets. For all the models, including DeepSurv, RSF, and CoxPH the same training and test sets were utilized. For the RSF method, hyperparameter selection was performed using grid search. The number of trees was set to 1000 and different values for the number of nodes (3–25) and the number of randomly selected variables as candidates to split a node (mtry) (2–4) were tried. The best performance was achieved with 15 nodes, and mtry set to 3 . The hyperparameters used for DeepSurv were determined by the random search method to be optimal for the model and not to cause overfitting. The hyperparameters were used as follows: the number of epochs (2000), L2 regularization (0.01), dropout (0.20), the number of hidden layers (100), the number of nodes (10), learning rate (0.0001), learning rate decay (0.0001), momentum (0.90), and activation function (ReLU). DeepSurv, RSF, and CoxPH models were compared with Harrell's C‐index. Harrell's C‐index introduced in Harrell et al., 1982, is a widely used metric for evaluating risk models in survival analysis, especially when dealing with censored data.The data were summarized as mean ± standard deviation and median (minimum–maximum) for continuous variables, and frequencies (percentiles) for categorical variables. Categorical variables were subjected to survival analyses using the Kaplan–Meier method, and the log‐rank test was employed to identify significant differences between groups. Multiple CoxPH model analysis was conducted to determine predictors of recurrence. Values of 3Of the 268 patients included in the study, 65 (24.3%) were female and the mean age was 61.69 ± 8.03 (27–83). Table p = 0.004), indicating a significant association with recurrence, while in multiple Cox regression analysis, pathological stage (p = 0.016), and SUVmax of the lesion (p = 0.006) were independent prognostic factors for recurrence (Table Recurrence was observed in 52 19.4%) patients. The 5‐year recurrence‐free survival rate of the patients was 74.7% ± 3.4. In the univariate analysis, a log‐rank test was conducted to assess the differences in survival distribution among the various types of pathological stage ; formal analysis (lead); methodology (lead); software (lead); visualization (lead); writing – original draft (lead). Gökhan Kocaman: Conceptualization (supporting); data curation ; formal analysis (supporting); methodology (supporting); resources ; visualization (supporting). Farrukh İbrahimov: Conceptualization (supporting); data curation (supporting); investigation (supporting); resources (supporting). Serkan Enön: Data curation (supporting); resources (supporting); supervision ; writing – review and editing . Erdal Coşgun: Conceptualization ; methodology ; software ; supervision ; writing – review and editing . Atilla Halil Elhan: Conceptualization ; methodology ; supervision ; visualization ; writing – review and editing .The authors declare no conflict of interest.


This study aimed to investigate the incidence, consequences, and predictors of serious chemotherapy‐induced thrombocytopenia (CIT) in nasopharyngeal carcinoma (NPC).We retrospectively reviewed the clinical records of patients with NPC between 2013 and 2015. Multivariate Cox proportional hazards regression model and propensity score matching were used to estimate the effect of serious CIT on overall survival. Univariate and multivariate logistic regression analyses were applied to identify the predictors of serious CIT.The incidence of serious CIT was 5.21% in patients with NPC. Patients who experienced serious thrombocytopenia had a worse long‐term prognosis, while the difference in short‐term survival rate was slight. Chemotherapy regimens of gemcitabine and platinum, 5‐fluorouracil and platinum, taxane and platinum, serum potassium ion concentration, serum lactate dehydrogenase levels, platelet count, red blood cell count, and estimated glomerular filtration rate were predictors of serious CIT. The exclusion criterion was a change in the chemotherapy regimen for reasons other than thrombocytopenia. According to the Common Terminology Criteria for Adverse Events (CTCAE version 5.0), thrombocytopenia grade 3 is defined as platelet count between 25 × 109/L and 50 × 109/L, and grade 4, platelet count <25 × 109/L.We retrospectively reviewed the records of newly diagnosed patients with non‐metastatic NPC from the Sun Yat‐Sen University Cancer Center (SYSUCC) between 2013 and 2015. The inclusion criteria were as follows: (1) aged from 18 to 75 years when diagnosed; (2) pretreatment white blood cell (WBC) count ≥4 × 102.2To analyze the baseline characteristics of patients, the Mann–Whitney–Wilcoxon test was used for count data and the chi‐squared test was used for categorical data. Univariate and multivariate Cox proportional hazards regression models were used to estimate the effect on overall survival (OS), and Schoenfeld residuals were calculated to test the proportional hazards assumption. To further demonstrate the above result, survival analysis was evaluated using the Kaplan–Meier method and examined with the log‐rank test after adjusting for confounding factors using propensity score matching (PSM). Univariate and multivariate logistic regression analyses were applied to identify the predictors of serious CIT. To find the threshold of predictors, receiver operating characteristic (ROC) curve analysis was performed, and the area under the curve (AUC) was calculated. OS was defined as the time from diagnosis to death from any cause or the date of last follow‐up for patients alive or lost to follow‐up. All statistical analyses were conducted using R (version 4.1.3) with tableone, MatchIt, survminer, survival, rms, and pROC packages.33.1n = 12), serious neutropenia (n = 4), request for a shorter hospital stay (n = 3), and unknown reasons (n = 8). Finally, a total of 3933 patients were included in this study. Table n = 205) within 120 days from the start of treatment. The incidences of serious CIT among patients receiving platinum alone, gemcitabine and platinum (GP), platinum and 5‐fluorouracil (PF), taxane and platinum (TP), taxane plus platinum and 5‐fluorouracil (TPF) were 2.66%, 13.97%, 10.17%, 5.02%, and 3.72%.A total of 27 patients were excluded from analyses due to a change in chemotherapy regimen for reasons other than thrombocytopenia. The reasons for a change of chemotherapy were as follows: poor response to the first‐line chemotherapy n = 2, seriou3.2p < 0.001). The multivariate Cox model was adjusted for factors including age, stage, chemotherapy regimen, sex, pretreatment serum lactate dehydrogenase (LDH), and whether patients experienced grade 3–4 CIT, neutropenia, leukopenia, or anemia within 120 days from the start of treatment. A higher LDH level was LDH ≥245 U/L.9/L and grade 3–4 leukopenia was defined as WBC count <2.0 × 109/L. Grade 3–4 anemia was defined as hemoglobin level <80 g/L.p = 0.0028), a higher LDH (LDH ≥ 245 U/L) level , age , and stage IVa  were statically significant risk factors for OS. Sex, chemotherapy regimen, and Grade 3–4 neutropenia, leukopenia, and anemia were non‐significantly associated with OS (p > 0.05).OS was analyzed using univariate and multivariate Cox proportional hazards regression models. CIT was a risk factor in the univariate Cox regression analysis .A total of 587:201 patients (2.92:1) were included after PSM. The patient characteristics were balanced. Table p < 0.05).The median follow‐up time, calculated using the reverse Kaplan–Meier method, was 2298 days . The 5‐year OS of patients with serious CIT was 82.5% , which was significantly lower than that of patients without serious CIT, with a 5‐year OS of 90.9% . The 1‐year OS of patients with serious CIT was 98.98% , and patients without serious CIT was 99.1% . Survival curves were described by Kaplan–Meier curves and compared using the log‐rank test, as shown in Figure 3.4To find the underlying cause of decreased OS in patients with CIT, we measured the effects of CIT on chemotherapy and radiotherapy.p > 0.05). Sixty‐four patients received chemotherapy after CIT and 50% (n = 32) patients had delays in chemotherapy  due to CIT. The difference in OS was insignificant between patients with and without delays in chemotherapy before and after adjusting for important factors . Three patients had unplanned radiotherapy interruption due to side effects other than CIT. Tree patients had incomplete records of radiotherapy duration. One patient who stopped radiotherapy due to CIT received a total dose of 28 Gy for the gross tumor volume and died within 4 years. 172 patients continued radiotherapy despite experiencing CIT. Twenty‐nine patients had an interruption of radiotherapy due to CIT. A significantly longer radiotherapy treatment time (RTT) was observed in patients with radiotherapy interruption . However, the effect of radiotherapy interruption on OS was insignificant before and after adjusting for important factors . Table Six patients changed the chemotherapy regimen because of CIT and three patients died. 148 patients finished the planned chemotherapy treatment. Fifty‐four patients had chemotherapy dose reduction or complete chemotherapy stop. Patients who had chemotherapy reduction or stop had poorer OS (75.9% vs. 83.1%). However, the decrease in OS was statistically insignificant before and after adjusting for important factors (50% n = 3 patients3.5+), serum sodium ion concentration (Na+), and estimated glomerular filtration rate (eGFR). The Chronic Kidney Disease Epidemiology Collaboration creatinine–cystatin C equation was applied to assess eGFR.To investigate the predictors of serious CIT, we performed univariate and multivariate logistic regression analyses with 15 variables, including age, sex, stage, chemotherapy regimen, body mass index (BMI), and pretreatment data, including LDH, white blood cell (WBC) count, red blood cell (RBC) count, lymphocyte (LYM) count, platelet (PLT) count, absolute neutrophil count (ANC), serum indirect bilirubin (IBIL), serum potassium ion concentration , PF , and TP  chemotherapy, K+ , LDH (≥245 U/L) , RBC count , eGFR , and PLT count  were predictive factors of serious CIT. For clinical convenience, we determined the threshold of PLT count and eGFR using ROC curve analysis. The cut‐off value of PLT count was 228.75 × 109/L  and eGFR was 94.60 mL/min/1.73 m2 .Variables with 4+ was a risk factor for CIT.Serious CIT has long been a hot topic. However, to our knowledge, our study is the first to report the effects of CIT on long‐term survival and the first to report that higher serum KWhether patients who experienced neutropenia or leukopenia had worse prognoses was controversial.2 improved survival.In our study, the RTT was prolonged in 29 patients with CIT. Previous studies reported a worse OS in patients with prolonged RTT whose threshold was 49–56 days.2) was a risk factor for CIT.2 , which meant the effect of eGFR on CIT also exists in patients with normal renal function. The mechanism might be that better renal function results in more chemotherapy drug excretion, and fewer side effects on megakaryocytes. We also found that eGFR was not a predictive factor of OS through multivariate Cox proportional hazards regression analysis. More chemotherapy drug excretion may not cause a worse prognosis. Further research on the relationship between eGFR and drug concentration in serum is needed to prove this hypothesis.In a previous study, lower eGFR ; data curation (lead); formal analysis (lead); investigation (lead); methodology (lead); resources (lead); visualization (lead); writing – original draft (lead). Xi Chen: Methodology ; resources . Ying‐Ying Huang: Resources . Chi‐Xiong Liang: Resources . Meng‐Yun Qiang: Resources . Zhuo‐Chen Cai: Resources (supporting). Ze‐Jiang Zhan: Resources (supporting). Ying Deng: Resources (supporting). Jia‐Yu Zhou: Resources (supporting). Hao‐Yang Huang: Resources (supporting). Xiang Guo: Funding acquisition ; resources . Xing Lv: Conceptualization ; funding acquisition ; supervision (lead); writing – review and editing (lead).This work was supported by the National Natural Science Foundation of China (Nos. 81872375 and 82172863) and the Natural Science Foundation of Guangdong Province (2021A1515010118).The authors have no conflict of interest to declare.This study was approved by the ethics committee of Sun Yat‐sen University Cancer Center .Written informed consent was not required because it was a retrospective study. The study was conducted in accordance with the Declaration of Helsinki.


In subgroup analysis, LBM was increased in studies that were ≥ 24‐week duration  and in participants with BMI lower than 27 kg/m2 . SB supplementation may yield increases in BMI in predialysis CKD patients. However, our findings did not support the beneficial effects of SB supplementation on other anthropometric outcomes. There is an evident need for long‐term high‐quality interventions to confirm these findings.Metabolic acidosis (MA) may play a key role in the pathogenesis of protein‐energy wasting (PEW) in patients with chronic kidney disease (CKD). To present a comprehensive synthesis of the effect of oral sodium bicarbonate (SB) supplementation on anthropometric measures in patients with CKD, a systematic review was undertaken in PubMed/MEDLINE, Web of Science, Cochrane CENTRAL, and Google Scholar, of relevant articles published prior to September 2022. The summary statistics of effect size, nonstandardized weighted mean difference (WMD), and 95% confidence interval (CI) were used to compare the effects of SB supplementation on anthropometric parameters vs. control group. To detect probable sources of heterogeneity, a series of predefined subgroup analyses were conducted. In total, 17 studies with 21 treatment arms, including 2203 participants , met our inclusion criteria and were included in the meta‐analysis. SB supplementation had no significant effect on body weight (BW), midarm muscle circumference (MAMC), or lean body mass (LBM) in patients with CKD. There was a significant increase in body mass index (BMI) (MD: 0.59 kg/m Sodium bicarbonate supplementation may be efficacious in increasing BMI in predialysis CKD patients. However, our findings did not support the beneficial effects of sodium bicarbonate supplementation on other anthropometric outcomes. A systematic search of studies published between 2000 until September 24, 2022 was performed on PubMed/MEDLINE, Web of Science, Cochrane CENTRAL, and Google Scholar. Electronic searches were complemented by hand searches of the reference lists of eligible articles. Search terms were a combination of keywords relevant to sodium bicarbonate and study design to identify related publications. Further details about the search strategy are provided in Table 2.2Relevant articles were included if they: (1) compared oral bicarbonate supplementation therapy with placebo, usual patient care or no study medication on predialysis CKD patients; (2) provided sufficient information including mean, standard deviation (SD), and the number of participants in each study of change in LBM, BW, MAMC, and BMI across study arms or reported sufficient information to estimate those values; (3) were randomized controlled trial; (4) be available as a full‐text publication; and (5) were published in any language. The exclusion criteria were as follows: (1) reported duplicate data ; (2) involved either dialysis or kidney transplantation patients; (3) evaluated the effects of intravenous SB supplementation; (4) were conducted in participants under the age of 18 years (children and adolescents) or in pregnant or lactating women; (5) unable to extract the corresponding information; (6) did not have placebo or untreated control groups; and (7) were reviews, letters, editorial articles, case reports, observational studies, books, conferences, or nonrandomized trials.2.3Using Cochrane Collaboration's tool 1 (ROB1) . The following relevant data were extracted using a standard data extraction sheet: first author, year of publication, study location, health status of study participants, number of participants, study design, sex of participants, duration of study, form of SB and placebo administration, type of comparator, background diet, dosage of SB administration, and outcomes. When the data were reported at multiple measurements, only the outcomes at the end of the intervention were included in the analysis. In the case of multiple publications with duplicate and/or overlapped data for the same trial, the publication with most comprehensive and complete data was selected. Data were cross‐checked to minimize potential errors, and disagreements were resolved through discussion with the third author (S.M.H.R).2.5I2 statistic values. An I2 value <50% indicated nonsignificant heterogeneity, a value ≥50% was considered as moderate heterogeneity, and a value ≥75% was considered as substantial heterogeneity between studies. To detect probable sources of heterogeneity, a series of predefined subgroup analyses were conducted based on health status , age (<55 years old vs. ≥55), BMI (<27 kg/m2 vs. ≥27), dose of SB (<0/7 mEq/kg bw/day vs. ≥0/7 mEq/kg bw/day), and follow‐up duration (<44 weeks vs. ≥44 or < 24 weeks vs. ≥24 weeks). Influence analysis was conducted to test the potential impact of each trial on the pooled effect size. The potential for publication bias was tested using Egger's test, Begg's test, and by visual inspection of funnel plots. Also, we examined possible publication bias using significance funnel plots, Figure p < 0.05 was used to demarcate statistical significance.All statistical analyses were carried out using STATA version 17 . Mean change of outcomes within intervention groups and placebo groups was calculated by subtracting the mean values at the end of follow‐up from those at the baseline. Comparison between SB groups and control groups was obtained by subtracting calculated differences within intervention groups from control groups. Calculation of the standard deviations (SDs) was conducted using the following formula: SD = square root ([SD pretreatment]2 + [SD posttreatment]2‐[2R × SD pretreatment × SD posttreatment]), using a correlation coefficient of the change‐value SDs that were available from the other studies based on the presented method in the Cochrane guidelines  were conducted in Europe, six studies (35.2%) were conducted in North America, and five studies (29.4%) were conducted in Asia. The intervention dose of sodium bicarbonate varied from 0.4 to 1.2 mEq/kg bw/day, depending on the prescribed form of sodium bicarbonate.In total, 17 studies with 21 treatment arms, including 2203 participants , met our inclusion criteria and were included in our meta‐analysis. All included studies were of parallel design. Among the 17 RCTs, two (11.7%) included participants with CKD stages 1 and 2, 11 studies (64.7%) included participants with CKD stages 3–5, two studies (11.7%) included participants with CKD and diabetes, and two studies (11.7%) included participants with CKD and hypertension. The mean age of participants in RCTs ranged from 49 to 67 years and had a baseline BMI ranging from 21.2 to 33.6 kg/m3.33.3.1p = 0.84 and 0.16 kg, 95% CI: −0.31 to 0.64, p = 0.52, overall and in studies with less than 12‐week duration, respectively) and between‐study heterogeneity was low  was examined in 13 studies with 15 treatment arms, containing 1484 subjects . The pooled analysis demonstrated that sodium bicarbonate supplementation yielded no significant effect on BW compared to placebo  and Egger's test (p = 0.339) suggested no evidence of publication bias.Sensitivity analysis suggested that no individual trial had a substantial impact on the overall pooled effect size. Begg's test , with low between‐study heterogeneity  , examined the effect of sodium bicarbonate supplementation on BMI. Body mass index was significantly increased after sodium bicarbonate supplementation compared to the control group  compared to the short duration (<44 weeks), and when conducted in people with BMI lower than 27 kg/m2  compared to those with BMI ≥ 27 kg/m2  (MD: 0.76 kg/mm2 Table .p = 0.5) or Begg's test (p = 0.3).Sensitivity analysis suggested that no individual trial had a substantial impact on the overall pooled effect size. Furthermore, there was no indication of publication bias using the Egger's test . The study duration and baseline BMI were possible sources of heterogeneity . Pooled results indicated that sodium bicarbonate supplementation had no significant effect on MAMC compared to placebo .Sensitivity analysis suggested that overall estimates for MAMC were not affected by the removal of any individual study. No statistically significant publication bias was evident (3.5p = 0.07) and that between‐study heterogeneity was significant  Figure Using data from three studies, with four treatment arms, containing 309 participants (166 cases and 143 controls), meta‐analysis suggested that sodium bicarbonate supplementation had no significant effect on LBM   and in participants with BMI lower than 27 kg/m1) Table .p = 0.73) and Egger's test (p = 0.59) suggested no evidence of publication bias.Sensitivity analyses revealed that the exclusion of any single study from the analysis did not alter the overall effect. Begg's test (3.6Based on the Cochrane risk‐of‐bias tool 1 (ROB 1), 12 studies (70.5%) were rated as having good overall quality and five studies (29.4%) had fair overall quality. Table 3.7Examining significance funnel plots Figure , the sum42. We found no significant effect of sodium bicarbonate supplementation on BW, MAMC, and LBM. However, in subgroup analysis, LBM was increased in studies with ≥24‐week duration and in participants with BMI lower than 27 kg/m2. To our knowledge, this is the first systematic review and meta‐analysis studying the effects of sodium bicarbonate supplementation on anthropometric measures and muscle mass specifically in patients with CKD.The findings from the present systematic review and meta‐analysis indicate that oral sodium bicarbonate supplementation might improve BMI in predialysis CKD patients, especially in trials with longer duration and in participants with a starting BMI lower than 27 kg/mMetabolic acidosis is a common complication of CKD, which can lead to increased inflammation, protein malnutrition, sarcopenia, PEW, acceleration of progression of CKD, and finally CKD morbidity and mortality. Metabolic acidosis is among the key mechanisms which have been proposed to explain adverse nutritional changes in these patients. Correction of MA with base administration was associated with improved kidney function and bone disease, increased insulin sensitivity, and decreased muscle wasting ; methodology ; software ; writing – original draft . Mohammad Hossein Rouhani: Conceptualization (lead); data curation ; methodology ; supervision ; writing – review and editing (lead). Firouzeh Moeinzadeh: Writing – review and editing . Cain C. T. Clark: Writing – review and editing (supporting). Rahele Ziaei: Data curation ; formal analysis (lead); methodology (lead); supervision (lead); writing – original draft .The present study was supported by a grant from Vice Chancellor for Research, Isfahan University of Medical Sciences.None.Appendix S1Click here for additional data file.


Deschampsia antarctica and the Antarctic moss Sanionia uncinata, as well as bulk soil sample, devoid of vegetation. We evaluated bacterial antagonistic interactions, focusing on species from the genera Actinomyces, Bacillus, and Pseudomonas. We also measured soil physicochemical properties and evaluated bacterial abundance and diversity using high-throughput sequencing. Our results suggested that Antarctic dominant plants significantly influenced bacterial antagonistic interactions compared to bulk soils. Using structural equation modelling (SEM), we compared and analyzed the direct effect of grasses and mosses on bacterial antagonistic interactions and the indirect effects through changes in edaphic properties and bacterial abundance. SEMs showed that (1) grasses and mosses had a significant direct influence on bacterial antagonistic interactions; (2) grasses had a strong influence on soil water content, pH, and abundances of Actinomyces and Pseudomonas and (3) mosses influenced bacterial antagonistic interactions by impacting abundances of Actinomyces, Bacillus, and Pseudomonas. This study highlights the role of dominant plants in modulating bacterial antagonistic interactions in Antarctic terrestrial ecosystems.In Antarctic terrestrial ecosystems, dominant plant species (grasses and mosses) and soil physicochemical properties have a significant influence on soil microbial communities. However, the effects of dominant plants on bacterial antagonistic interactions in Antarctica remain unclear. We hypothesized that dominant plant species can affect bacterial antagonistic interactions directly and indirectly by inducing alterations in soil physicochemical properties and bacterial abundance. We collected soil samples from two typical dominant plant species; the Antarctic grass  Antarctica is the coldest, driest, and windiest continent and is considered one of the harshest environments for biodiversity , yet it in situ.Microbial interactions within the soil community primarily involve antagonistic interactions . The pheActinomyces (Actinobacteria) are considered the most prolific producers of secondary metabolites, including antifungals, antivirals, and antibiotics (Bacillus (Firmicutes) is also one of the most common and functionally diverse bacterial genera of the soil. Members of this genus are well known for their capacity to produce antimicrobial compounds, participate in the cycling of nutrients, and protect plants from pathogens in different ecosystems (Pseudomonas (Gammaproteobacteria) species can be involved in antagonistic activities, especially antifungal activity in soil . We compared a dominant flowering plant species, the Antarctic hair grass uncinata . The pri regions , and the regions .Deschampsia antarctica and Sanionia uncinata). To do so, we selected 3 experimental sites dominated by the abovementioned plant species. At each site, we randomly established five (10 m × 10 m) plots with more than 10 plant individuals. Within each (10 m × 10 m) plot, three (30 cm × 30 cm) quadrats were established: one quadrat was placed around an adult moss individual, one quadrat was placed around an adult grass individual and one quadrat was placed on bare soil. 200 g of soil was collected from each quadrat [3 sites × 5 plots × (2 plants + 1 bulk soil quadrats)], close to the center of the base of plants . To ensure that all plots shared the same climatic conditions, there was less than a 10 m distance between experimental sites. Each soil sample was divided into three equal-sized aliquots for (1) the analysis of soil physicochemical properties, (2) DNA sequencing, and (3) the characterization of bacterial antagonistic potential (The study was conducted on the Fildes Peninsula (62°08′ to 62°14′S and 59°02′ to 58°51′W), near the Great Wall Chinese research station, southwest of King George Island, Antarctica, during the austral summer of 2019–2020. To determine the direct and indirect effects of Antarctic dominant plants on antagonistic bacterial communities, we collected soil samples with and without the dominant plants , nitrates (NO3−), and pH. Total nitrogen and total phosphorus were digested by concentrated H2SO4 and measured by semi-micro Kjeldahl and Mo-Sb antispetrophotography (Each soil sample was assessed for the determination of soil properties according to routine procedures. 10 g of soil was dried for 72 h at 105°C to determine the moisture content (SWC) of the soil . Then, ttography , respecttography . Both amtography . A pH me® Kit  following the manufacturer’s protocol. For the composition of the bacterial community, the V4 region of bacterial 16S rRNA genes was amplified using specific primers: 515F (GTGYCAGCMGCCGCGGTAA) and 806R (GGACTACNVGGGTWTCTAAT). The PCR products were visualized using a 2% agarose gel. Then, the Qiagen Gel Extraction Kit  was used to purify the PCR product mixture. The TruSeq® DNA PCR-Free Sample Preparation Kit  was used to prepare the sequencing libraries. The library quality was assessed on the Qubit@2.0 Fluorometer  and Agilent Bioanalyzer 2,100 system . The library was sequenced on an Illumina NovaSeq platform  using PE250 strategy. Artificial sequences and low-quality sequences were removed using the fastp software  at 99% similarity were generated using the dada2-paired pipeline integrated to the QIIME2 software (QIIME2.2021.2).Genomic DNA was extracted using the DNeasy PowerSoilsoftware . We obtaActinomyces, Bacillus, and Pseudomonas. For simplicity, we use the term ‘bacterial’ with abundance, diversity and antagonism in many places throughout the manuscript to refer to these groups. We used the dilution method of Actinomyces: 175 rpm for 60 min; Bacillus and Pseudomonas: 200 rpm for 120 min). For the enumeration of Actinomyces, 100 μl of each soil dilution was plated on starch-casein agar containing 100 μg ml−1 of cycloheximide and incubated for 7 days at 20°C. For Bacillus, the soil suspension was first incubated at 85°C for 30 min. Dilutions of the suspension were then plated on Tryptic soy agar and incubated for 4 days at 20°C. Finally, for Pseudomonas, the dilutions were plated on King’s B agar containing 100 μg ml−1 of cycloheximide, 13 μg ml−1 of chloramphenicol, and 40 μg ml−1 of ampicillin, and incubated for 5 days at 20°C.The soil aliquots designated for the antagonistic potential investigation were further divided into three equal-sized aliquots for a total of 135 soil samples for bacterial antagonistic evaluation. Samples were kept at 4°C and each soil sample was evaluated for cultivable bacterial densities. Three cultivable bacterial groups were quantified, comprising members of the genera Fusarium graminearum (ACCC 36249), Rhizoctonia solani (strain 3.1704) and Streptomyces fildesensis (strain INACH3013) (Fusarium species (Ascomycota) and Rhizoctonia species (Basidiomycota) are ubiquitously present in Antarctic soils, particularly in King George Island  Petri dish, incubated at room temperature for 7 days was homogenized (for a few seconds) with 100 ml of distilled water in a sterile blender. Then, 500 ml of molten potato dextrose agar (PDA), cooled to 45°C, was inoculated with 20 ml of the Fusarium solution obtained and poured over the enumeration plates. The plates were incubated at 25°C for 4 days. For Rhizoctonia, liquid cultures in Czapek-Dox (CD) broth (incubated for 7 days at room temperature) were homogenized in a sterile blender (for a few seconds). 100 ml of this Rhizoctonia solution was added per 500 ml of molten CD agar . This medium was poured over the enumeration plates and incubated at room temperature (25°C) for 4–5 days. For Streptomyces, 150 μl of a 20% glycerol suspension containing approximately 108 CFU/ml of Streptomyces fildesensis was plated over the enumeration plates and incubated at 28°C for 5 days (optimum growth temperature was determined in pilot experiments)  . Fusariue Island , while triments) . Three sThe antagonistic potential for all samples  was evaluated. The radius of the inhibition zone was used to assess antagonism . For eacData analysis and plotting were conducted in R, version 4.0.2 . StatistActinomyces, Bacillus and Pseudomonas  analyses in accordance with the following assumptions: we hypothesized that (1) dominant plants have a significant influence on the antagonistic bacterial potential of udomonas ; (2) Antudomonas ; (3) bacudomonas ; (4) plamunities . The effudomonas .There are several indices that evaluate the goodness of fit for structural equation models; however, no single index is approved universally. Therefore, to evaluate the accuracy of SEM, we combined the root mean square error of approximation (RMSEA) test and χ2 test. A good model fit to the data was indicated by a non-significant χ2 and RMSEA test . The gooActinomyces, Bacillus, and Pseudomonas densities and antagonistic activities differed between vegetated and bulk soils. Among all experimental treatments, the highest total cultivable Actinomyces densities were recorded in grass-associated soils with a mean value of 1.7 × 106 CFU/g against 0.66 × 106 CFU/g in bulk soils  shared a large number of bacteria , Firmicurmicutes  and Protrmicutes . HoweverActinobacteria, Alphaproteobacteria and Gammaproteobacteria in vegetated soils. The RDA also showed the significant influence of dominant plants on edaphic characteristics. Soil water content, soil organic carbon, total nitrogen, ammonium, and pH were significantly influenced by dominant plant species , Firmicutes , and Proteobacteria (Alphaproteobacteria) also had a strong correlations with indices of antagonistic interactions and edaphic properties  can be found below: NCBI - PRJNA921735.SC and BN designed the study and performed laboratory experiments. IzA, HS, YW, XL, MU, IkA, SX, and KL performed field experiments. BN, ZL, and SX performed statistical analysis and interpretation of data. BN and LM drafted the manuscript. LA, SX and SC provided scientific research funds. All authors contributed to the article and approved the submitted version.This research was supported by the Project of the National Natural Science Foundation of China . This research was also supported by Second Tibetan Plateau Scientific Expedition and Research (STEP) Program (2019QZKK0302).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Long‐term follow‐up of multiple myeloma (MM) clinical trials are needed to assess long‐term outcomes. We aimed to investigate the length of follow‐up of all phase III MM clinical trials. Median follow‐up duration of clinical trials of newly diagnosed MM was longer when compared to relapsed/refractory MM clinical trials . The follow‐up duration of phase III clinical trials in MM is relatively short when compared to the improved outcomes in the current era. Efforts should be made to facilitate long‐term clinical trials follow‐up and/or publication of results of updated results. Outcomewww.clinicaltrials.gov for all phase III completed clinical trials that studied MM. Search terms included myeloma, multiple myeloma, plasma cell dyscrasia. We limited our studies to phase III studies reported as completed. Data cut‐off data of September, 2022. Systematic review was conducted using study identifier/s, principal investigator name, and/or study title using PubMed, Cochrane, and Google Scholar search engines to identify the most recent publication to be included in the analysis. Given the publicly available data, this study was considered exempt from the University of Arkansas for Medical Sciences.We searched www.clinicaltrials.gov and the final reported actual median follow‐up duration for survival outcomes and/or response‐related outcomes retrieved from the latest publication. We collected data on estimated original enrollment, actual number of patients enrolled, study start date and completion date, date of last published update, study location/s, disease status (newly diagnosed vs. relapsed/refractory), race/ethnicity of enrolled patients, study primary outcome/s, and reported follow‐up durations. All statistical analyzes were done using R statistical software version 4.0.5  [Data on clinical trials characteristics were collected, including planned median follow‐up duration reported at A total of 169 studies were retrieved Table . Out of n = 63), the most common primary outcome was either PFS, time to progression or event free survival in 54 studies (85.7%), whereas OS was the primary endpoint in 9 studies (14.3%). Race and/or ethnicity of participating patients were reported only in 27 (21.8%) clinical trials. A total of 94 studies (75.8%) had subsequent publications with the duration of follow‐up reported in only 56 publications. The rest of clinical trials (n = 30) only reported intended follow‐up duration at www.clinicaltrials.gov with no subsequent publication/presentation.Of survival‐related outcomes , 36 (range: 10.9–132), 24 (range: 6–70) months for OS, PFS, and duration of response (DOR), respectively. The most recent median follow‐up durations retrieved from the latest publication were 44.3 (range: 27.8–76), 44 (range: 15.9–120), and 28 (range: 10.4–77.2) months for OS, PFS, and DOR, respectively. The actual median follow‐up duration for all outcomes from latest publication is 35.2 months (range: 10.4–120 months). Median follow‐up duration of NDMM clinical trials was longer when compared to R/R MM clinical trials  (Figure The planned median follow‐up durations from 8 range: 0.9–132, Our analysis showed that median follow‐up duration of phase III completed clinical trials in MM is relatively short when compared to the actual survival of MM patients. For NDMM, the median follow‐up duration was approximately 3.5 years. In patients with NDMM, survival outcomes continue to improve , 3, 5, 6Randomized clinical trials can be very expensive. The cost of longer follow‐up duration may add to the overall cost of conducting clinical trials especially with the need of logistical considerations. The value of long‐term follow‐up of clinical trials in the field of oncology is highly significant. Multiple large clinical trials reported long‐term follow‐up durations which make this feasible , 7. ClinOur analysis showed that a significant number of subsequent publications for phase III clinical trials in MM did not report a follow‐up duration. Moreover, about one fourth of included clinical trials did not have subsequent publications/presentation. Future efforts should be made to facilitate publications of all phase III clinical trials regardless of the results and to ensure the reporting of follow‐up durations in updated publications.Our analysis has few limitations. We did not account for any plans to report on longer follow‐up duration. Most of the actual publications reported longer follow‐up durations in most recent publications when compared to planned follow‐up provided in clinicaltrials.gov. We only included phase III clinical trials in our analysis. Despite those limitations, we believe that our analysis provides important information on the current follow‐up duration for all completed phase III clinical trials in the field of MM.In conclusion, we found that the follow‐up duration of phase III clinical trials in MM is relatively short when compared to the improved outcomes in the current era. Efforts should be made to facilitate long‐term clinical trials follow‐up and/or publication of results of updated results.Samer Al Hadidi conceived the research idea. Mohammad O. Ali, Khaldun Obeidat, and Hafez M. Abdullah worked on data collection and study selection. Mohammad O. Ali and Samer Al Hadidi worked on review of literature, statistical analysis, and manuscript drafting. All authors wrote the initial draft of the manuscript. Rajshekhar Chakraborty and Samer Al Hadidi critically reviewed and approved the final submission of the manuscript.Authors declare that they have no conflict of interests.Data request can be made to the corresponding author.Supporting InformationClick here for additional data file.


The objective of this research was to analyze the prognostic significance of the minimum count of lymphocytes (LY) and the corresponding ratio of neutrophil‐to‐lymphocyte (NLR) in cervical cancer (CC) patients receiving radiotherapy.We retrospectively collected data from 202 CC patients who received concurrent chemoradiotherapy or radiotherapy alone at our hospital. Statistical methods including the Kaplan–Meier method, log‐rank test and the Cox proportional hazards model were included to examine survival differences and identify independent factors that may affect overall survival (OS) and progression‐free survival (PFS).The research enrolled a total of 202 patients. Patients with higher LY levels and lower NLR values during radiotherapy had significantly better survival prognosis than those with lower LY levels and higher NLR values. Multivariate COX regression analysis revealed that FIGO stage I, pathological types of SqCC, absence of lymph node metastasis, concurrent chemoradiotherapy, higher LY levels during radiotherapy, and lower NLR values before radiotherapy were independently associated with poorer PFS. Similarly, FIGO stage I, absence of lymph node metastasis and lower NLR values during and before radiotherapy were independently linked with poorer OS.Minimum LY value and its corresponding NLR during radiotherapy serve as prognostic factors for CC. According to data, around 600,000 new cases were reported in 2020 globally, with approximately 340,000 recorded deaths.Numerous researches have demonstrated the pivotal role of the tumor microenvironment in the occurrence, progression, metastasis, and prognosis of the cancer, and that inflammation is a crucial component of this environment.In this study, representative hematological results during treatment were selected as research objects to generate more valuable research results. Because radiation induces lymphocytopenia, the lowest value of lymphocytes (LY) during radiation therapy may better reflect the immune status of the body during radiation therapy. Hence, we conducted this retrospective investigation to examine the predictive value of the minimum LY value and corresponding NLR value during radiotherapy on the prognosis of patients with CC. Meanwhile, LY and corresponding NLR values in hematological reports within 1 week before radiotherapy were collected and compared with those during radiotherapy.22.1We reviewed clinical data on patients with primary cervical cancer in Stages I through IV of FIGO 2009, who underwent radiation therapy or concurrent chemoradiotherapy at our facility between June 2008 and September 2018. All patients underwent surgical treatment prior to radiotherapy, mainly consisting of radical hysterectomy and pelvic lymph node dissection. Radiotherapy was performed within 3 months after surgery. Patients who did not undergo surgery before radiotherapy or received other additional treatments alongside surgery, those with immune dysfunction, hematologic disease, severe hepatic or renal insufficiency, lack of pathological diagnosis, incomplete radiotherapy plan, incomplete blood parameter results, combined with second tumor, or who were lost follow‐up were excluded. In total, 202 patients with Stage I–II were enrolled, and clinical data were collected, including age, FIGO stage, pathological type, and lymph node status. Lymphocyte and neutrophil counts were collected during the 1 week prior to radiotherapy, as well as lymphocyte minimums and corresponding neutrophil values during the entire radiotherapy period, and the ratio of NLR was calculated.2.22) was the primary treatment regimen for patients who received synchronous chemotherapy, with three to five cycles depending on the treatment response. Some patients received brachytherapy and adjuvant chemotherapy after external irradiation.All enrolled patients underwent external pelvic irradiation by intensity modulated radiation therapy (IMRT). The average external radiation dose was 50.4 Gy, administered in proportions of 1.8–2.0 Gy. Weekly cisplatin . Meanwhile, the time from initial treatment to the last follow‐up or death was defined as overall survival (OS).2.4IBM SPSS software (version 26.0) was utilized in this research for the analysis of statistical. The predictive value of LY and NLR for prognosis was analyzed by ROC curve analysis, and the optimum cutoff value and corresponding specificity and sensitivity were obtained by using the maximum Youden index (sensitivity + specificity‐1) as the boundary. After employing the Kaplan–Meier to compute the survival disparity among groups, the log‐rank test was utilized to identify any significant variations between the groups. We carried out single‐variable and multiple‐variable analyses using COX proportional hazard models and examined 95% confidence intervals (CI) and hazard ratios (HR).3We reviewed the medical records and laboratory reports of 202 patients who met our inclusion standard and received radiation therapy at our facility. The patients' features are presented in Table 9/L and 5.2, respectively. The lowest lymphocyte count during radiotherapy ranged from 0.1 × 109/L to 1.5 × 109/L, with the majority of patients (83%) experiencing it during the third to fifth week of radiotherapy.For all follow‐up period time, the median time was 71 months. By the end of follow‐up, 33 patients had been passed away, and the median OS duration was 78 months (with a range of 10–175 months). The OS rates for 3‐ and 5‐year periods, as well as PFS, were 93%, 89%, 85%, and 74%, correspondingly. We analyzed the value of stage, histology, lymph nodes, treatment strategy, LY, and NLR for clinical outcomes in patients with CC. Patients with FIGO II and lymph node metastases had poorer PFS and OS, while patients with tissue squamous cell carcinoma and concurrent chemoradiotherapy had better PFS. Histological type of squamous cell carcinoma and concurrent chemoradiotherapy showed beneficial effects on PFS but had no significant effect on OS. We also measured the median LY and NLR values of patients before and during radiotherapy. The median LY and NLR values before radiotherapy were 1.5 × 109/L and 2.24, respectively, while during radiotherapy, the values were 0.4 × 103.1p < 0.05), respectively. The cutoff values were 1.45 × 109/L for pre radiotherapy LY , and 0.35 × 109/L for during radiotherapy .Figure p < 0.01), respectively. The cutoff values were 3.029 for pre radiotherapy NLR  and 5.071 for during radiotherapy NLR . These results indicate that LY and NLR values are significant indicators in forecasting the prognosis of CC.Figure 3.29/L), low LY group (<1.45 × 109/L), high NLR group (≥3.029), and low NLR group (<3.029) before radiotherapy, and high LY group (≥0.35 × 109/L), low LY group (<0.35 × 109/L), high NLR group (≥5.071), and low NLR group (<5.155) during treatment based on threshold value obtained through ROC analysis (<5.071). As shown in Tables p = 0.036, 0.046, 0.013, and 0.042, respectively). Pathological type was correlated with the value of LY before radiotherapy (p = 0.026), and lymph node status was correlated with the values of LY and NLR before radiotherapy  and the value of LY during radiotherapy (p = 0.041).All patients were categorized into high LY group  and lower NLR levels (≤3.029) had significantly better OS in CC . Similarly, during radiotherapy, the cutoff points of OS predicted by LY and NLR values were 0.35 × 109/L and 5.071, respectively. The OS of CC patients was also significantly improved in the higher LY (>0.35 × 109/L) and lower NLR (≤5.071) groups , as shown in Figure After grouping all 202 patients based on the ROC curve's cutoff value, a survival analysis was carried out to assess the predictive significance of LY and NLR. The cutoff points of OS predicted by pre radiotherapy values of LY and NLR were 1.45 × 103.4p = 0.041 and 0.035, respectively). However, no relationship was found between the NLR value before radiotherapy or LY value during radiotherapy and recurrence pattern .Out of the 202 patients included in this study, a total of 62 patients experienced local or distant relapse. For patients with multiple relapses, the relapse pattern after the initial radiation therapy was selected to be included to analyzed. As shown in Table 3.5p = 0.000), pathological type of SqCC (p = 0.006), absence of lymph node metastasis (p = 0.002), concurrent chemoradiotherapy (p = 0.003), higher lymphocyte values during radiotherapy (p = 0.008), and lower NLR values pre radiotherapy (p = 0.000) were independent predictors for improving PFS. In addition, FIGO stage I (p = 0.044), absence of lymph node metastasis (p = 0.011) and lower NLR values before (p = 0.042) and during radiotherapy (p = 0.019) were independent predictors for improving OS.As the Kaplan–Meier survival analysis indicated the significant impact of LY and NLR on the survival of CC patients, COX regression analysis was carried out to assess their prognostic value in conjunction with other factors. The findings of univariate and multivariate analyses examining the potential predictors related to PFS are exhibited in Tables 4Prior research has proposed that the prognosis of CC is highly linked with factors such as tumor stage, pathological type, degree of differentiation, and the presence of lymph node metastasis.The materiality of immunotherapy in the treatment of tumors has been gaining more attention due to the close association between the human immune system and tumor progression. Recently, scholars have shown interest in combining immunotherapy with traditional chemoradiotherapy to treat CC. For instance, a recent study demonstrated that using ipilimumab and chemoradiotherapy together improved PFS and OS in CC patients with lymph node metastases.In recent years, a growing body of literature has shown that an elevated NLR value is linked with the progression and metastasis of various tumors.Zhu et al.'s study demonstrated a correlation between pretreatment NLR values and FIGO stage and parametrial involvement in CC, although no association with lymph node metastasis was observed.+ T cells may inhibit tumor growth and induce tumor cell apoptosis through cytotoxic effects, while CD4+ T cells can secrete cytokines necessary for the growth and proliferation of CD8+ T cells.+ T cells could control tumor growth and progression by secreting th1 cytokines to reactivate tumor cell senescence.+ T cells and other immune cells into the tumor microenvironment, leading to improved therapeutic outcomes.Despite the significant predictive value of changes in LY and NLR values before and after CC treatment, there have been few studies focusing on the predictive significance of LY and NLR values during radiotherapy. This study aimed to address this gap by analyzing the correlation between LY and NLR values before and during radiotherapy, clinicopathology factors, and prognostic value in a cohort of 202 CC patients who underwent surgery. Although previous studies and this study have demonstrated the correlation between LY and NLR values and tumor clinicopathological factors and prognosis, the underlying mechanisms have not been fully elucidated. Current research suggests that it could be primarily explained by the function of LY subpopulations. CD8While our study provides important insights into the prognostic significance of lymphocyte count and NLR in CC patients undergoing radiotherapy, there are several limitations of the research that must be acknowledged. First, the number of participants in this study was limited, and all of them were enrolled from a single hospital, which could constrain the overall applicability of our results. Therefore, larger multicenter, future researches are needed to validate our results. Second, our study was retrospective, which may introduce selection bias. Future studies should consider using a prospective study design to minimize such bias. Third, the hematological results were derived from the detection of peripheral blood samples, which may not completely represent the immune status of the tumor microenvironment. Further studies are required to explore the relationship between the peripheral blood and tumor microenvironment and the predictive value and mechanism of changes in lymphocyte count and NLR to PFS and OS in CC patients.5In summary, our study revealed that the minimum LY level and corresponding NLR values during radiotherapy hold significant predictive value for the prognosis of CC patients. Additionally, a higher LY value during radiotherapy was discovered to be linked independently with poorer PFS, while a higher NLR value during radiotherapy was linked to poorer OS. As both LY and NLR are routine detection indexes in blood examinations, their values can be dynamically monitored to predict patient prognosis. Furthermore, the LY and NLR values during radiotherapy can serve as simple, convenient, and repeatable indicators for identifying potentially high‐risk patients. However, before this approach can be routinely applied in clinical practice, more large‐scale, multicenter prospective studies are needed to confirm its efficacy.Mengli Zhao: Conceptualization ; writing – original draft (lead). Zhongrong Gao: Visualization . Xiaowei Gu: Data curation ; visualization . Xiaojing Yang: Data curation . Shanshan Wang: Data curation (supporting). Jie Fu: Project administration ; writing – review and editing (lead).This work was supported by the Science and Technology Project of Shanghai Municipal Science and Technology Commission (No. 22Y31900500).The authors declare that the research was conducted without any conflict of interest.The study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Shanghai Sixth People's Hospital (protocol code 2022‐KY‐149(K) and 2022‐10‐20 of approval).Not applicable.


Validating the vulnerabilities and pathologies underlying treatment‐resistant schizophrenia (TRS) is an important challenge in optimizing treatment. Gyrification and surface area (SA), reflecting neurodevelopmental features, have been linked to genetic vulnerability to schizophrenia. The aim of this study was to identify gyrification and SA abnormalities specific to TRS.We analyzed 3T magnetic resonance imaging findings of 24 healthy controls (HCs), 20 responders to first‐line antipsychotics (FL‐Resp), and 41 patients with TRS, including 19 clozapine responders (CLZ‐Resp) and 22 FL‐ and clozapine‐resistant patients (patients with ultratreatment‐resistant schizophrenia [URS]). The local gyrification index (LGI) and associated SA were analyzed across groups. Diagnostic accuracy was verified by receiver operating characteristic curve analysis.P = 0.041, Hedges g [gH] = 0.75; P = 0.013, gH = 0.96) and FL‐Resp  in the left medial parietal cortex (Lt‐MPC). In addition, both CLZ‐Resp and URS had lower SA in the Lt‐MPC than FL‐Resp . LGI and SA were positively correlated in non‐TRS (FL‐Resp)  and TRS (CLZ‐Resp + URS) . The areas under the receiver operating characteristic curve for non‐TRS versus TRS with LGI and SA in the Lt‐MPC were 0.79 and 0.85, respectively. SA in the Lt‐MPC was inversely correlated with negative symptoms  and clozapine plasma levels  in TRS.Both CLZ‐Resp and URS had lower LGI values than HCs (LGI and SA in the Lt‐MPC, a functional hub in the default‐mode network, were abnormally reduced in TRS compared with non‐TRS. Thus, altered LGI and SA in the Lt‐MPC might be structural features associated with genetic vulnerability to TRS. Schizophrenia is a debilitating neuropsychiatric disorder, the underlying pathological mechanisms of which remain unclear.et al.Several neuroimaging studies have explored brain abnormalities characterized by TRS using various modalities, such as magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS), diffusion tensor imaging, and positron emission tomography.On the other hand, there are a limited number of studies that have identified brain structural characteristics unique to TRS and URS. Several studies have reported altered cortical and subcortical structures in TRS compared with non‐TRS (i.e. FL‐Resp), such as reduced cortical thickness in the left dorsolateral prefrontal cortex,Cortical gyrification is a morphological feature that indicates the developmental process of cortical folding patterns.et al.Gyrification abnormalities in patients with schizophrenia have been extensively studied, although anomalous patterns of gyrification vary depending on the different clinical characteristics of patients with schizophrenia.et al.Given these pieces of evidence, examining gyrification and its associated SA in individuals with schizophrenia based on their response to antipsychotics is of great importance and may shed light on understanding TRS pathology. The aim of this study was to identify the brain structural features specific to TRS or URS by comparing gyrification and SA between FL‐Resp, CLZ‐Resp, URS, and healthy controls (HCs). The degree of gyrification was quantified by the LGI. We also analyzed the SA in the brain regions where abnormal LGI specific to TRS was observed and examined its relationship with LGI. Notably, Palaniyappan The protocol for the research project was approved by the research ethics board at the Centre for Addiction and Mental Health (CAMH), which conform to the provisions of the Declaration of Helsinki. All participants received explanations about the procedures involved in this research and gave informed consent before participation. Data from this study were obtained from a cross‐sectional MRS study that examined glutamatergic neurometabolite levels in URSDSM‐IV criteria for schizophrenia based on the Structured Clinical Interview for DSM‐IV. The criteria for FL‐Resp, CLZ‐Resp, and URS are described below. HCs were screened using the Mini‐International Neuropsychiatric Interview and had neither a history of psychiatric illness nor neurological disorders, including epilepsy. The exclusion criteria for all groups were as follows: (1) substance abuse or dependence during the past 6 months; (2) positive urine drug screen at inclusion or prior to MRI scanning; (3) neurological illness; (4) head trauma with loss of consciousness for more than 30 min; (5) any contraindications of MRI, such as implanted metal in the body, tattoos, pregnancy, and claustrophobia; and (6) inability to provide informed consent.The final sample of participants consisted of 24 HCs , 20 FL‐Resp , 19 CLZ‐Resp  and 22 URS . The participants were aged 18 years or older at the time of MRI scanning. The patient groups and HC group were matched in terms of age and sex. All participants received explanations about the procedures involved in this research and gave informed consent before participation. All patients met the Antipsychotic treatment resistance and responsiveness were defined by the modified Treatment Response and Resistance in Psychosis (TRRIP) Working Group Consensus criteria.The FL‐Resp were taking an antipsychotic, but not clozapine, for >6 consecutive weeks and met the criteria of treatment response. The FL‐Resp had no history of treatment resistance. The CLZ‐Resp were taking clozapine for >6 weeks and were treatment responsive after a history of treatment resistance to at least two other antipsychotics where chlorpromazine antipsychotic dose equivalents of >400 mg were administered for >6 consecutive weeks. The URS showed resistance to clozapine treatment after experiencing resistance to at least two other antipsychotics in the same manner as the CLZ‐Resp. They were treated with >300 mg/day clozapine for >6 weeks.The participants were scanned using a 3T scanner  equipped with an 8‐channel head coil at the CAMH. A three‐dimensional inversion recovery–prepared T1‐weighted MRI scan was conducted using the GE brain volume (BRAVO) sequence .https://surfer.nmr.mgh.harvard.edu/) for preprocessing, LGI and SA analysis, and whole‐brain statistical analysis. T1‐weighted MRI images were preprocessed with the standard automatic reconstruction algorithm of FreeSurfer, which included skull stripping, volumetric labeling, intensity normalization, white matter segmentation, cortical surface atlas registration, surface extraction, and gyral labeling. The quality of the reconstructed images was visually checked by a trained researcher (K. K.) who was blinded to the demographic information of the participants. If there were any inaccuracies in brain tissue segmentation, the researcher manually edited them. After manual editing, 2 HCs, 1 FL‐Resp, 5 CLZ‐Resp, and 2 URS participants were excluded from further analysis because accurate separation between gray matter and white matter was not possible, resulting in 24 HCs, 20 FL‐Resp, 19 CLZ‐Resp, and 22 URS participants. We quantified the individual vertexwise LGI values of the entire cortex using the method of Schaer et al.We used FreeSurfer software  were performed to test the differences and the ratios between each pair of groups.We conducted one‐way ANOVA and et al.P < 0.001. We performed a total of 1000 simulations for each comparison using a threshold of P < 0.05 to define significant clusters.In the whole‐brain statistical analysis using a general linear model, a contrast matrix was set up to estimate a main effect of the group for LGI in each vertex. In addition, we also used the contrast matrixes to compare the LGI in each vertex between each pair of groups, between all patients with schizophrenia (FL‐Resp + CLZ‐Resp + URS) and HCs, between TRS (CLZ‐Resp + URS) and FL‐Resp, and between TRS (CLZ‐Resp + URS) and HCs. Regarding the use of covariates, Hyatt http://cran.r-project.org/). Unpaired t tests with multiple comparison corrections using the Bonferroni method were performed to compare the differences in the LGI and SA between each pair of groups. The effect sizes are expressed as gH.We regarded the cluster in which a significant main effect of the group was observed as an ROI, and then the individual values of the LGI and SA were extracted from the ROI. The extracted values were submitted to one‐way ANOVA with group  as a between‐patient factor using R software  curve analysis using a nonparametric approach. In addition to each LGI or SA, we also conducted ROC curve analysis with the combination of LGI and SA as discriminant values. We calculated the Youden index for each cutoff value ([sensitivity + specificity] – 1) to find the cutoff values that maximized the discriminating power. We compared statistically area under the curves (AUCs) of the three ROC curves.The detailed demographic and clinical characteristics are described in Table A whole‐brain statistical analysis of LGI values revealed that there was a significant main effect of group in multiple regions of the medial part of the left hemisphere. We examined the anatomical parcellation of these regions based on the Destrieux atlas in FreeSurferF  = 7.23, P < 0.001). The post hoc tests with Bonferroni correction revealed that the CLZ‐Resp had a significantly lower LGI value in the Lt‐MPC than the HCs  and the FL‐Resp . The URS also had significantly lower LGI values in the Lt‐MPC than the HCs  and FL‐Resp . There were no significant differences between the HCs and FL‐Resp  or between the CLZ‐Resp and URS .One‐way ANOVA on the LGI values in the Lt‐MPC extracted from an ROI Fig.  showed aF  = 11.24, P < 0.001). The post hoc tests with Bonferroni correction revealed that the CLZ‐Resp had a significantly lower SA value in the Lt‐MPC than the FL‐Resp . The URS also had a significantly lower SA value in the Lt‐MPC than the HCs  and FL‐Resp . There were no significant differences between the HCs and FL‐Resp , between the HCs and CLZ‐Resp , or between the CLZ‐Resp and URS .One‐way ANOVA on the SA extracted from the ROI Fig.  showed aρ = 0.64, corrected P = 0.008) and TRS (CLZ‐Resp + URS)  .There were significant positive correlations between LGI and SA values in the Lt‐MPC for the non‐TRS (FL‐Resp) (01) Fig. . A margiP < 0.001 ), indicating that the discriminant value of LGI could be used to differentiate between the non‐TRS and TRS with moderate accuracy.P < 0.001 ), indicating that the discriminant value of SA could be used to differentiate between the non‐TRS and TRS with excellent accuracy. The Youden index indicated a favorable cutoff point of 1368.50, which resulted in 92.7% sensitivity and 75.0% specificity. Figure P < 0.001 ), indicating that the discriminant value of SA could be used to differentiate between the non‐TRS and TRS with excellent accuracy. The Youden index indicated a favorable cutoff point of 0.606, which resulted in 87.8% sensitivity and 80.0% specificity. There were no significant differences of AUCs between the LGI and SA , between the LGI and the combination of LGI and SA , or between the SA and the combination of LGI and SA .We used ROC curve analyses to explore the discriminant value of the LGI. Figure ρ = −0.40, corrected P = 0.018) but not in the non‐TRS    or TRS (CLZ‐Resp + URS) groups. Regarding the SA, there was a significant negative correlation between SA in the Lt‐MPC and PANSS negative scores in the TRS (56) Fig. . There w42) Fig. . No signThe current study indicates that the LGI in the Lt‐MPC, composed of the left precuneus, posterior cingulate gyrus, and parieto‐occipital sulcus, was decreased in both the CLZ‐Resp and the URS compared with the HCs and FL‐Resp. The SA in the same region was decreased in both the CLZ‐Resp and the URS compared with the FL‐Resp. Significant positive correlations between LGI and SA were also observed in the non‐TRS (FL‐Resp) and TRS (CLZ‐Resp + URS). The ROC analyses revealed that LGI and SA values in the Lt‐MPC could differentiate the TRS (CLZ‐Resp + URS) from the non‐TRS (FL‐Resp) with moderate and excellent accuracy, respectively. Regarding clinical features, SA values in the Lt‐MPC were correlated with the severity of negative symptoms in patients with TRS. Given that gyrification patterns and their relations with the SA reflect neurodevelopmental processes or characteristics associated with genetic vulnerability,We found abnormal gyrification unique to TRS in the Lt‐MPC. The decrease gyrification in the Lt‐MPC was also observed in a limited number of previous studies that reported decreased LGI values in multiple regions, including the Lt‐MPC in schizophrenia.Regarding gyrification characteristics for TRS, a previous longitudinal studyWhile the present study found hypogyrification in TRS, several schizophrenia studies have reported hypergyrification.et al.In the course of brain development, the brain undergoes several maturational events to establish neural networks,et al.et al.Notably, the most recent perspectives on the DMN are that the left precuneus and posterior cingulate cortex, consisting of the Lt‐MPC region , are the central functional regions for integrating external and internal information, allowing for shared communication and social interaction.Importantly, LGI deficit was also accompanied by decreased SA unique to TRS in the Lt‐MPC. Decreased SA was also observed in previous studies, showing a decreasing trend of SA values in schizophrenia.We found an inverse correlation between the SA value in the Lt‐MPC and PANSS negative scores in TRS, whereas there were no such correlations shown in non‐TRS (FL‐Resp) Fig. . These rNotably, one of the current study's objectives was to detect brain structures that can predict the degree of treatment resistance within schizophrenia, allowing for a therapeutic strategy of early clozapine intervention. Our ROC analysis revealed that the LGI and SA values in the Lt‐MPC could differentiate between TRS and non‐TRS with moderate (AUC = 0.79) and excellent (AUC = 0.85) accuracies, respectively. In addition, the combination of LGI and SA could differentiate them with excellent accuracies (AUC = 0.85) Fig. . These rSome caveats of the current study should be noted. First, although we included the expected number of total participants (n = 95), the number of participants included in each group may be relatively small. Further study with a larger sample size is needed to confirm our results toward clinical application. Second, since this is a study regarding TRS, all of the patients were taking antipsychotics at the MRI scan, and therefore, we could not completely exclude the possible influence of antipsychotics on the results reported here. In particular, it cannot be completely denied that clozapine intake could result in different LGI between FL‐Resp and TRS (CLZ‐Resp and URS). Although it may be challenging, recruiting patients with TRS who were not prescribed clozapine would be needed to solve this limitation in future studies. Third, in previous studies, including the current study, there has been great heterogeneity in the definitions of TRS and URS, and the implementation of guidelines has been inadequate.The current results demonstrate that the LGI and its associated SA in the Lt‐MPC, a functional hub region of the DMN, were abnormally decreased in patients with TRS (i.e. CLZ‐Resp and URS) compared with non‐TRS (FL‐Resp). The ROC analysis also revealed that the LGI and SA values in the Lt‐MPC could differentiate between the TRS and non‐TRS with moderate and excellent accuracies, respectively. Thus, the altered LGI in the Lt‐MPC might be a potential structural marker that could assist in predicting patients' response to first‐line antipsychotics,G.R. has received grants from HLS Therapeutics, Inc. F.U. has received grants from Nakatani Foundation. S.N. has received grants from Japan Research Foundation for Clinical Pharmacology, Naito Foundation, Takeda Science Foundation, Uehara Memorial Foundation, and Daiichi Sankyo Scholarship Donation Program within the past 3 years.Conception and design of study: K.K., S.T., D.S., S.N., G.R., A.G.G., and Y.H. Acquisition and analysis of data: K.K., S.T., and Y.H. Literature review and interpretation: K.K., S.T., D.S., S.N., Y.I., F.U., Y.T., J.T., and Y.H. Drafting the first manuscript or figures: K.K. Critical revision of the manuscript: K.K, S.T., D.S., S.N., Y.I., F.U., Y.T., J.T., F.C., W.M., E.T.C., Y.N., P.G., V.L., M.M., S.H., T.N., T.O., G.R., A.G.G., and Y.H. All of the authors contributed to and approved the final manuscript.Figure S1. Comparisons of whole‐brain local gyrification index (LGI) values between ultratreatment‐resistant schizophrenia (URS) and first‐line antipsychotics (FL‐Resp) and between treatment‐resistant schizophrenia (TRS) and non‐TRS. (a) Cluster showing significantly lower LGI in patients with URS compared with patients with schizophrenia who responded to FL‐Resp. (b) Cluster showing significantly lower LGI in patients with TRS compared with patients with schizophrenia who responded to non‐TRS (FL‐Resp). The maps are shown for the left hemispheres in the medial view. The horizontal bar shows cluster P‐value.Click here for additional data file.Table S1. Clusters with significant group differences in LGIClick here for additional data file.


To the Editor,Peanut allergen Ara h 6 is known to maintain IgE‐binding capacity upon exposure to digestive enzymesWhen five donors consumed peanut prior to blood donation , serum Ara h 6 levels were elevated up to 14 h after consumption  to detect Ara h 6, and while this demonstrates that Ara h 6 in circulation possesses IgG epitopes, this does not necessarily mean that Ara h 6 in circulation still has allergenic activity. Such can be shown by basophil activation tests, however, within the current setting at our laboratories there was no access to those tests. Others have shown that levels of Ara h 6 detected in circulation after peanut consumption correlate with basophil activation potencyAdhering to clinical guidelines for routine blood donation, 320 mL units of plasma were also obtained from these donors, at the same intervals after peanut consumption, and a similar pattern of Ara h 6 appearance was observed  were analyzed for Ara h 6 content. As per blood donation guidelines, donors had no dietary restrictions and did not receive instruction to consume or avoid peanut. In 17 of the 20 individual plasma samples, Ara h 6 was detected  of Ara h 6 was found.It is not known which concentration of peanut allergen in circulation is needed to trigger a systemic reaction in vivo, and if such levels can be reached by blood transfusion, but an estimation can be made using thresholds for basophil activation reported in literature. For example, the in vitro study of Hemmings et al.These modeled concentrations of Ara h 6 are in the same order of magnitude as, or somewhat above, the threshold for basophil activation, suggesting that transfusion using a pooled plasma product can trigger an allergic reaction in peanut‐allergic recipients. At this stage, no clinical data are available to demonstrate reactivity of Ara h 6 in transfusion products. For example, the role of donor and recipient IgG, which may prevent Ara h 6 to bind to effector cells, needs further exploration. Follow‐up studies could include basophil activation assays with (Ara h 6‐positive) transfusion material, or similar in vivo experiments with peanut‐sensitized animals.The importance of other digestion‐resistant peanut allergens, like Ara h 2, should be explored as well, as these allergens can further potentiate the allergenic load of transfusion products. For now, only Ara h 6 was considered because no methods are available to detect and quantify other peanut allergens in blood samples.Fleur A. C. Jansen: Data curation (lead); investigation (lead); validation ; visualization ; writing—original draft . Klaske van Norren: Methodology ; supervision ; writing—review and editing . Joseph L. Baumert: Conceptualization ; writing—review and editing . Annegeet van den Bos: Resources ; writing—review and editing . Joannes F. M. Jacobs: Resources ; writing—review and editing . Stef J. Koppelman: Conceptualization ; methodology ; supervision ; validation ; writing—original draft (lead).Fleur A. C. Jansen, Klaske van Norren, Joseph L. Baumert, Annegeet van den Bos and Joannes F. M. Jacobs report no conflict of interest. Stef J. Koppelman is consultant to DBV Technologies, outside the scope of the current paper.Supporting Information S1Click here for additional data file.