Effect of Acupuncture for Methadone Reduction : A Randomized Clinical Trial.

BACKGROUND: Methadone maintenance treatment (MMT) is effective for managing opioid use disorder, but adverse effects mean that optimal therapy occurs with the lowest dose that controls opioid craving. OBJECTIVE: To assess the efficacy of acupuncture versus sham acupuncture on methadone dose reduction. DESIGN: Multicenter, 2-group, randomized, sham-controlled trial. (Chinese Clinical Trial Registry: ChiCTR2200058123). SETTING: 6 MMT clinics in China. PARTICIPANTS: Adults aged 65 years or younger with opioid use disorder who attended clinic daily and had been using MMT for at least 6 weeks. INTERVENTION: Acupuncture or sham acupuncture 3 times a week for 8 weeks. MEASUREMENTS: The 2 primary outcomes were the proportion of participants who achieved a reduction in methadone dose of 20% or more compared with baseline and opioid craving, which was measured by the change from baseline on a 100-mm visual analogue scale (VAS). RESULTS: Of 118 eligible participants, 60 were randomly assigned to acupuncture and 58 were randomly assigned to sham acupuncture (2 did not receive acupuncture). At week 8, more patients reduced their methadone dose 20% or more with acupuncture than with sham acupuncture (37 [62%] vs. 16 [29%]; risk difference, 32% [97.5% CI, 13% to 52%]; P < 0.001). In addition, acupuncture was more effective in decreasing opioid craving than sham acupuncture with a mean difference of -11.7 mm VAS (CI, -18.7 to -4.8 mm; P < 0.001). No serious adverse events occurred. There were no notable differences between study groups when participants were asked which type of acupuncture they received. LIMITATION: Fixed acupuncture protocol limited personalization and only 12 weeks of follow-up after stopping acupuncture. CONCLUSION: Eight weeks of acupuncture were superior to sham acupuncture in reducing methadone dose and decreasing opioid craving. PRIMARY FUNDING SOURCE: National Natural Science Foundation of China.

Development and validation of an inflammatory biomarkers model to predict gastric cancer prognosis: a multi-center cohort study in China.

BACKGROUND: Inflammatory factors have increasingly become a more cost-effective prognostic indicator for gastric cancer (GC). The goal of this study was to develop a prognostic score system for gastric cancer patients based on inflammatory indicators. METHODS: Patients' baseline characteristics and anthropometric measures were used as predictors, and independently screened by multiple machine learning(ML) algorithms. We constructed risk scores to predict overall survival in the training cohort and tested risk scores in the validation. The predictors selected by the model were used in multivariate Cox regression analysis and developed a nomogram to predict the individual survival of GC patients. RESULTS: A 13-variable adaptive boost machine (ADA) model mainly comprising tumor stage and inflammation indices was selected in a wide variety of machine learning models. The ADA model performed well in predicting survival in the validation set (AUC = 0.751; 95% CI: 0.698, 0.803). Patients in the study were split into two sets - "high-risk" and "low-risk" based on 0.42, the cut-off value of the risk score. We plotted the survival curves using Kaplan-Meier analysis. CONCLUSION: The proposed model performed well in predicting the prognosis of GC patients and could help clinicians apply management strategies for better prognostic outcomes for patients.

Three-year field study on the temporal response of soil microbial communities and functions to PFOA exposure.

Contamination of per- and polyfluoroalkyl substances (PFAS) poses a significant threat to soil ecosystem health, yet there remains a lack of understanding regarding the responses of soil microbial communities to prolonged PFAS exposure in field conditions. This study involved a three-year field investigation to track changes in microbial communities and functions in soil subjected to the contamination of a primary PFAS, perfluorooctanoic acid (PFOA). Results showed that PFOA exposure altered soil bacterial and fungal communities in terms of diversity, composition, and structure. Notably, certain bacterial communities with a delayed reaction to PFOA contamination showed the most significant response after one year of exposure. Fungal communities were sensitive to PFOA in soil, exhibiting significant responses within just four months of exposure. After two years, the impact of PFOA on both bacterial and fungal communities was lessened, likely due to the long-term adaptation of microbial communities to PFOA. Moreover, PFOA exposure notably inhibited alkaline phosphatase activity and reduced certain phosphorus cycling-related functional genes after three years of exposure, suggesting potential disruptions in soil fertility. These new insights advance our understanding of the long-term effects of PFOA on soil microbial communities and functions at a field scale.

The potential of machine learning models to identify malnutrition diagnosed by GLIM combined with NRS-2002 in colorectal cancer patients without weight loss information.

BACKGROUND & AIMS: The key step of the Global Leadership Initiative on Malnutrition (GLIM) is nutritional risk screening, while the most appropriate screening tool for colorectal cancer (CRC) patients is yet unknown. The GLIM diagnosis relies on weight loss information, and bias or even failure to recall patients' historical weight can cause misestimates of malnutrition. We aimed to compare the suitability of several screening tools in GLIM diagnosis, and establish machine learning (ML) models to predict malnutrition in CRC patients without weight loss information. METHODS: This multicenter cohort study enrolled 4487 CRC patients. The capability of GLIM diagnoses combined with four screening tools in predicting survival probability was compared by Kaplan-Meier curves, and the most accurate one was selected as the malnutrition reference standard. Participants were randomly assigned to a training cohort (n = 3365) and a validation cohort (n = 1122). Several ML approaches were adopted to establish models for predicting malnutrition without weight loss data. We estimated feature importance and reserved the top 30% of variables for retraining simplified models. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to assess and compare model performance. RESULTS: NRS-2002 was the most suitable screening tool for GLIM diagnosis in CRC patients, with the highest hazard ratio (1.59; 95% CI, 1.43-1.77). A total of 2076 (46.3%) patients were malnourished diagnosed by GLIM combined with NRS-2002. The simplified random forest (RF) model outperformed other models with an AUC of 0.830 (95% CI, 0.805-0.854), and accuracy, sensitivity and specificity were 0.775, 0.835 and 0.742, respectively. We deployed an online application based on the simplified RF model to accurately estimate malnutrition probability in CRC patients without weight loss information (https://zzuwtt1998.shinyapps.io/dynnomapp/). CONCLUSIONS: Nutrition Risk Screening 2002 was the optimal initial nutritional risk screening tool in the GLIM process. The RF model outperformed other models, and an online prediction tool was developed to properly identify patients at high risk of malnutrition.

Complete biodegradation of tetrabromobisphenol A through sequential anaerobic reductive dehalogenation and aerobic oxidation.

Tetrabromobisphenol A (TBBPA), a common brominated flame retardant and a notorious pollutant in anaerobic environments, resists aerobic degradation but can undergo reductive dehalogenation to produce bisphenol A (BPA), an endocrine disruptor. Conversely, BPA is resistant to anaerobic biodegradation but susceptible to aerobic degradation. Microbial degradation of TBBPA via anoxic/oxic processes is scarcely documented. We established an anaerobic microcosm for TBBPA dehalogenation to BPA facilitated by humin. Dehalobacter species increased with a growth yield of 1.5 × 108 cells per µmol Br- released, suggesting their role in TBBPA dehalogenation. We innovatively achieved complete and sustainable biodegradation of TBBPA in sand/soil columns columns, synergizing TBBPA reductive dehalogenation by anaerobic functional microbiota and BPA aerobic oxidation by Sphingomonas sp. strain TTNP3. Over 42 days, 95.11 % of the injected TBBPA in three batches was debrominated to BPA. Following injection of strain TTNP3 cells, 85.57 % of BPA was aerobically degraded. Aerobic BPA degradation column experiments also indicated that aeration and cell colonization significantly increased degradation rates. This treatment strategy provides valuable technical insights for complete TBBPA biodegradation and analogous contaminants.

Exploring the optimal indicator of short-term peridiagnosis weight dynamics to predict cancer survival: A multicentre cohort study.

BACKGROUND: Body weight and its changes have been associated with cancer outcomes. However, the associations of short-term peridiagnosis weight dynamics in standardized, clinically operational time frames with cancer survival remain largely unknown. This study aimed to screen for and evaluate the optimal indicator of short-term peridiagnosis weight dynamics to predict overall survival (OS) in patients with cancer. METHODS: This multicentre cohort study prospectively collected data from 7460 patients pathologically diagnosed with cancer between 2013 and 2019. Body weight data were recorded 1 month before, at the time of and 1 month following diagnosis. By permuting different types (point value in kg, point height-adjusted value in kg/m2, absolute change in kg or relative change in percentage) and time frames (prediagnosis, postdiagnosis or peridiagnosis), we generated 12 different weight-related indicators and compared their prognostic performance using Harrell's C-index, integrated discrimination improvement, continuous net reclassification improvement and time-dependent C-index. We analysed associations of peridiagnosis relative weight change (RWC) with OS using restricted cubic spine (RCS), Kaplan-Meier analysis and multivariable-adjusted Cox regression models. RESULTS: The study enrolled 5012 males and 2448 females, with a median age of 59 years. During a median follow-up of 37 months, 1026 deaths occurred. Peridiagnosis (1 month before diagnosis to 1 month following diagnosis) RWC showed higher prognostic performance (Harrell's C-index = 0.601, 95% confidence interval [CI] = [0.583, 0.619]) than other types of indicators including body mass index (BMI), absolute weight change, absolute BMI change, prediagnosis RWC and postdiagnosis RWC in the study population (all P < 0.05). Time-dependent C-index analysis also indicated that peridiagnosis RWC was optimal for predicting OS. The multivariable-adjusted RCS analysis revealed an N-shaped non-linear association between peridiagnosis RWC and OS (PRWC < 0.001, Pnon-linear < 0.001). Univariate survival analysis showed that the peridiagnosis RWC groups could represent distinct mortality risk stratifications (P < 0.001). Multivariable survival analysis showed that, compared with the maintenance group (weight change < 5%), the significant (gain >10%, hazard ratio [HR] = 0.530, 95% CI = [0.413, 0.680]) and moderate (gain 5-10%, HR = 0.588, 95% CI = [0.422, 0.819]) weight gain groups were both associated with improved OS. In contrast, the moderate (loss 5-10%, HR = 1.219, 95% CI = [1.029, 1.443]) and significant (loss >10%, HR = 1.280, 95% CI = [1.095, 1.497]) weight loss groups were both associated with poorer OS. CONCLUSIONS: The prognostic performance of peridiagnosis RWC is superior to other weight-related indicators in patients with cancer. The findings underscore the importance of expanding the surveillance of body weight from at diagnosis to both past and future, and conducting it within clinically operational time frames, in order to identify and intervene with patients who are at risk of weight change-related premature deaths.

Integrated neutrophil-to-lymphocyte ratio and handgrip strength better predict survival in patients with cancer cachexia.

OBJECTIVES: Systemic inflammation and skeletal muscle strength play crucial roles in the development and progression of cancer cachexia. In this study we aimed to evaluate the combined prognostic value of neutrophil-to-lymphocyte ratio (NLR) and handgrip strength (HGS) for survival in patients with cancer cachexia. METHODS: This multicenter cohort study involved 1826 patients with cancer cachexia. The NLR-HGS (NH) index was defined as the ratio of neutrophil-to-lymphocyte ratio to handgrip strength. Harrell's C index and receiver operating characteristic (ROC) curve analysis were used to assess the prognosis of NH. Kaplan-Meier analysis and Cox regression models were used to evaluate the association of NH with all-cause mortality. RESULTS: Based on the optimal stratification, 380 women (NH > 0.14) and 249 men (NH > 0.19) were classified as having high NH. NH has shown greater predictive value compared to other indicators in predicting the survival of patients with cancer cachexia according to the 1-, 3-, and 5-y ROC analysis and Harrell's C index calculation. Multivariate survival analysis showed that higher NH was independently associated with an increased risk of death (hazard ratio = 1.654, 95% confidence interval = 1.389-1.969). CONCLUSION: This study demonstrates that the NH index, in combination with NLR and HGS, is an effective predictor of the prognosis of patients with cancer cachexia. It can offer effective prognosis stratification and guidance for their treatment.

A rapid PCR-free next-generation sequencing method for comprehensive diagnosis of chromosome disease syndromes in prenatal samples.

The aim of this study is to investigate the application performance of rapid copy number variation sequencing (rCNV-seq) technology for the detection of chromosomal abnormalities during prenatal diagnosis. Samples were collected from 424 pregnant women who were at high-risk for noninvasive prenatal screening in Kunming Maternal and Child Care Hospital from January 2018 to May 2022. rCNV-seq technique was used to detect fetal chromosome abnormalities and compare the results with that of chromosomal karyotype analysis. The Result showed that 330 (77.83%, 330/424) cases indicated chromosomal abnormalities among 424 high-risk pregnant women who underwent rCNV-seq. Moreover, 94 (22.17%, 94/424) cases were discovered to have copy number variations. Among the 330 fetuses with chromosomal abnormalities, common autosomal aneuploidy was observed in 203 cases (47.87%, 203/424) and sex chromosome aneuploidy was observed in 91 cases (21.46%, 91/424). Moreover, the abnormalities in multiple chromosomes were discovered in 33 cases (7.78%, 33/424), and the rare autosomal aneuploidy was observed in 3 cases (0.71%, 3/424). There were 63 fetuses (14.86%, 63/424) with pathogenic CNVs among the 94 fetuses with variable copy numbers. Of the 245 pregnant women who voluntarily selected G-band karyotyping, 1 fetus with copy number variation had normal karyotype results, and the remaining women were consistent with rCNV-seq. Our study revealed that rCNV-seq has higher accuracy in detecting common trisomy and can also detect chromosomal microdeletions or microduplications that cannot be detected by G-banding karyotype analysis. There is no effective treatment for chromosomal diseases, so it is particularly important to prevent chromosomal diseases through genetic counseling and prenatal diagnosis of chromosomal diseases.

Value of the modified Patient-Generated Subjective Global Assessment in indicating the need for nutrition intervention and predicting overall survival in patients with malignant tumors in at least two organs.

BACKGROUND: Although the Patient-Generated Subjective Global Assessment (PG-SGA) is a reference standard used to assess a patient's nutrition status, it is cumbersome to administer. The aim of the present study was to estimate the value of a simpler and easier-to-use modified PG-SGA (mPG-SGA) to evaluate the nutrition status and need for intervention in patients with malignant tumors present in at least two organs. METHODS: A total of 591 patients (343 male and 248 female) were included from the INSCOC study. A Pearson correlation analysis was conducted to assess the correlation between the mPG-SGA and nutrition-related factors, with the optimal cut-off defined by a receiver operating characteristic curve (ROC). The consistency between the mPG-SGA and PG-SGA was compared in a concordance analysis. A survival analysis was used to determine the effects of nutritional intervention among different nutrition status groups. Univariable and multivariable Cox analyses were applied to evaluate the association of the mPG-SGA with the all-cause mortality. RESULTS: The mPG-SGA showed a negative association with nutrition-related factors. Individuals with an mPG-SGA ≥ 5 (rounded from 4.5) were considered to need nutritional intervention. Among the malnourished patients (mPG-SGA ≥ 5), the overall survival (OS) of those who received nutrition intervention was significantly higher than that of patients who did not. However, the OS was not significantly different in the better-nourished patients (mPG-SGA < 5). CONCLUSION: Our findings support that the mPG-SGA is a feasible tool that can be used to guide nutritional interventions and predict the survival of patients with malignant tumors affecting at least two organs.

New techniques to identify the tissue of origin for cancer of unknown primary in the era of precision medicine: progress and challenges.

Despite a standardized diagnostic examination, cancer of unknown primary (CUP) is a rare metastatic malignancy with an unidentified tissue of origin (TOO). Patients diagnosed with CUP are typically treated with empiric chemotherapy, although their prognosis is worse than those with metastatic cancer of a known origin. TOO identification of CUP has been employed in precision medicine, and subsequent site-specific therapy is clinically helpful. For example, molecular profiling, including genomic profiling, gene expression profiling, epigenetics and proteins, has facilitated TOO identification. Moreover, machine learning has improved identification accuracy, and non-invasive methods, such as liquid biopsy and image omics, are gaining momentum. However, the heterogeneity in prediction accuracy, sample requirements and technical fundamentals among the various techniques is noteworthy. Accordingly, we systematically reviewed the development and limitations of novel TOO identification methods, compared their pros and cons and assessed their potential clinical usefulness. Our study may help patients shift from empirical to customized care and improve their prognoses.

Association of possible sarcopenia with all-cause mortality in patients with solid cancer: A nationwide multicenter cohort study.

OBJECTIVES: The concept of possible sarcopenia (PS) was recently introduced to enable timely intervention in settings without the technologies required to make a full diagnosis of sarcopenia. This study aimed to investigate the association between PS and all-cause mortality in patients with solid cancer. DESIGN: Retrospective observational study. SETTING AND PARTICIPANTS: 13,736 patients with 16 types of solid cancer who were ≥18 years old. MEASUREMENTS: The presence of both a low calf circumference (men <34 cm or women <33 cm) and low handgrip strength (men <28 kg or women <18 kg) was considered to indicate PS. Harrell's C-index was used to assess prognostic value and the association of PS with mortality was estimated by calculating multivariable-adjusted hazard ratios (HRs). RESULTS: The study enrolled 7207 men and 6529 women (median age = 57.8 years). During a median follow-up of 43 months, 3150 deaths occurred. PS showed higher Harrell's C-index (0.549, 95%CI = [0.541, 0.557]) than the low calf circumference (0.541, 95%CI = [0.531, 0.551], P = 0.037) or low handgrip strength (0.542, 95%CI = [0.532, 0.552], P = 0.026). PS was associated with increased mortality risk in both univariate (HR = 1.587, 95%CI = [1.476, 1.708]) and multivariable-adjusted models (HR = 1.190, 95%CI = [1.094, 1.293]). Sensitivity analyses showed that the association of PS with mortality was robust in different covariate subgroups, which also held after excluding those patients who died within the first 3 months (HR = 1.162, 95%CI = [1.060, 1.273]), 6 months (HR = 1.150, 95%CI = [1.039, 1.274]) and 12 months (HR = 1.139, 95%CI = [1.002, 1.296]) after enrollment. CONCLUSION: PS could independently and robustly predict all-cause mortality in patients with solid cancer. These findings imply the importance of including PS assessment in routine cancer care to provide significant prognostic information to help mitigate sarcopenia-related premature deaths.

Degradation and mechanism of PFOA by peroxymonosulfate activated by nitrogen-doped carbon foam-anchored nZVI in aqueous solutions.

Perfluorooctanoic acid (PFOA) is an emerging pollutant that is non-biodegradable and presents severe environmental and human health risks. In this study, we present an effective and mild approach for PFOA degradation that involves the use of nitrogen-doped carbon foam anchored with nanoscale zero-valent iron (nZVI@NCF) to activate low concentration peroxymonosulfate (PMS) for the treatment. The nZVI@NCF/PMS system efficiently removed 84.4% of PFOA (2.4 µM). The active sites of nZVI@NCF including Fe0 (110) and graphitic nitrogen played crucial roles in the degradation. Electrochemical analyses and density functional theory calculations revealed that nZVI@NCF acted as an electronic donor, transferring electrons to both PMS and PFOA during the reaction. By further analyzing the electron paramagnetic resonance and byproducts, it was determined that electron transfer and singlet oxygen were responsible for PFOA degradation. Three degradation pathways involving decarboxylation and surface reduction of PFOA in the nZVI@NCF/PMS system were determined. Finding from this study indicate that nZVI@NCF/PMS systems are effective in degrading PFOA and thus present a promising persulfate-advanced oxidation process technology for PFAS treatment.

Discovery of distinct cancer cachexia phenotypes using an unsupervised machine-learning algorithm.

OBJECTIVES: Cancer cachexia is a debilitating condition with widespread negative effects. The heterogeneity of clinical features within patients with cancer cachexia is unclear. The identification and prognostic analysis of diverse phenotypes of cancer cachexia may help develop individualized interventions to improve outcomes for vulnerable populations. The aim of this study was to show that the machine learning-based cancer cachexia classification model generalized well on the external validation cohort. METHODS: This was a nationwide multicenter observational study conducted from October 2012 to April 2021 in China. Unsupervised consensus clustering analysis was applied based on demographic, anthropometric, nutritional, oncological, and quality-of-life data. Key characteristics of each cluster were identified using the standardized mean difference. We used logistic and Cox regression analysis to evaluate 1-, 3-, 5-y, and overall mortality. RESULTS: A consensus clustering algorithm was performed for 4329 patients with cancer cachexia in the discovery cohort, and four clusters with distinct phenotypes were uncovered. From clusters 1 to 4, the clinical characteristics of patients showed a transition from almost unimpaired to mildly, moderately, and severely impaired. Consistently, an increase in mortality from clusters 1 to 4 was observed. The overall mortality rate was 32%, 40%, 54%, and 68%, and the median overall survival time was 21.9, 18, 16.7, and 13.6 mo for patients in clusters 1 to 4, respectively. Our machine learning-based model performed better in predicting mortality than the traditional model. External validation confirmed the above results. CONCLUSIONS: Machine learning is valuable for phenotype classifications of patients with cancer cachexia. Detection of clinically distinct clusters among cachexic patients assists in scheduling personalized treatment strategies and in patient selection for clinical trials.

Polygonal Approximation Learning for Convex Object Segmentation in Biomedical Images with Bounding Box Supervision.

As a common and critical medical image analysis task, deep learning based biomedical image segmentation is hindered by the dependence on costly fine-grained annotations. To alleviate this data dependence, in this paper, a novel approach, called Polygonal Approximation Learning (PAL), is proposed for convex object instance segmentation with only bounding-box supervision. The key idea behind PAL is that the detection model for convex objects already contains the necessary information for segmenting them since their convex hulls, which can be generated approximately by the intersection of bounding boxes, are equivalent to the masks representing the objects. To extract the essential information from the detection model, a repeated detection approach is employed on biomedical images where various rotation angles are applied and a dice loss with the projection of the rotated detection results is utilized as a supervised signal in training our segmentation model. In biomedical imaging tasks involving convex objects, such as nuclei instance segmentation, PAL outperforms the known models (e.g., BoxInst) that rely solely on box supervision. Furthermore, PAL achieves comparable performance with mask-supervised models including Mask R-CNN and Cascade Mask R-CNN. Interestingly, PAL also demonstrates remarkable performance on non-convex object instance segmentation tasks, for example, surgical instrument and organ instance segmentation. Our code is available at https://github.com/shenmishajing/PAL.

Occurrence of 8 trace elements in Rhizoma Cibotii from China and exposure assessment.

The contamination of trace elements in Chinese edible herbs has attracted worldwide concern over the world. The objective of the present study was to investigate the occurrence and exposure assessment of eight trace elements in Rhizoma Cibotii from China. For this purpose, the method of inductively coupled plasma mass spectrometry was employed to detect the contamination levels of target trace elements in 58 Rhizoma Cibotii samples. The results demonstrated that the trace elements of Cr, Ni, Cu, Zn, and Pb were detected in all analyzed samples; the occurrence frequencies of As, Se, and Cd were 98.3%, 96.6%, and 98.3%, respectively. The highest mean levels were found in Zn (17.32 mg/kg), followed by Pb (8.50 mg/kg) and Cu (3.51 mg/kg). For a further step, one-way ANOVA was used to compare the difference of eight elements levels among groups, and Pearson's correlation analysis was used to explore the correlation between elements in Rhizoma Cibotii. A strong positive correlation between Zn and Cd was observed by Pearson's correlation analysis, which indicated that the possible presence of Cd contamination in Rhizoma Cibotii. Based on the contamination levels, the mean exposure of individual element and the health risks of eight trace elements in Rhizoma Cibotii were estimated by health risk assessment models. The calculated HQ values were less than 1, indicating that the contamination of trace elements in Rhizoma Cibotii did not pose significant health risks to human. In conclusion, the study provided baseline information on the contamination levels of trace elements in Rhizoma Cibotii. Moreover, it is necessary to monitor the trend of trace elements levels in Rhizoma Cibotii, which will be useful for ingredient control and human health protection.

Nutritional status and its correlation to prognosis of nasopharyngeal carcinoma patients in different ages in China: a multicenter cohort study.

Nasopharyngeal carcinoma (NPC) patients usually presented malnutrition under chemoradiotherapy (CRT)/radiotherapy (RT). Few studies stratified by age to investigate the association of nutritional status with overall survival (OS) in NPC patients. This study aimed to explore the nutritional parameters related prognosis of NPC patients in different age. The total 1365 NPC patients were classified into young (18~45), middle-aged (46~60), and old groups (> 60). PG-SGA scores, NRS-2002 scores, Karnofsky performance status scores, anthropometric, and blood indicators (albumin, prealbumin, transferrin, C-reactive protein, hemoglobin, and total lymphocyte) were assessed. Cox regression analysis was performed to evaluate the association between risk factors of nutritional status and the overall survival in different age group of NPC patients. Kaplan-Meier (KM) survival analysis was used to estimate the effect of nutritional indexes on prognosis. The abnormal rate of albumin, prealbumin, hemoglobin, hand grip strength, and calf circumference increased with age. The malnutrition occurred in all age group and low calf circumference (HR, 4.427, 1.167-16.791) was an independent death risk in young adults. Distant metastasis (HR, 4.754, 2.737-8.260), low albumin (HR, 3.530, 1.708-7.296), hand grip strength (HR, 1.901, 1.160-3.115), and the nutritional intervention requirement (NRS-2002 ≥ 3) (HR, 2.802, 1.211-6.483) was significantly correlated with poor OS in NPC patients with middled age adults. Distant metastasis (HR, 2.546, 1.497-4.330), low albumin (HR, 1.824, 0.949-3.507), low hemoglobin (HR, 1.757, 1.015-3.044), low hand grip strength (HR, 1.771, 1.112-2.818), and low calf circumference (HR, 1.951, 1.074-3.545) were associated with increased risk of death in the elderly. KM analysis indicated that over 60 years, distant metastasis, low albumin, low hand grip strength, low calf circumference, and malnutritional risk (NRS-2002 ≥ 3) were correlated to prognosis of NPC patients. Low calf circumference could be a prognosis not only in elderly but also in young adults of NPC patients, whereas low albumin and distant metastasis were the prognostic factors in middle-aged and elderly patients. Patients aged over 60 years exhibited poorer OS compared with young and middle-aged adults.

Low-Data Drug Design with Few-Shot Generative Domain Adaptation.

Developing new drugs for emerging diseases, such as COVID-19, is crucial for promoting public health. In recent years, the application of artificial intelligence (AI) has significantly advanced drug discovery pipelines. Generative models, such as generative adversarial networks (GANs), exhibit the potential for discovering novel drug molecules by relying on a vast number of training samples. However, for new diseases, only a few samples are typically available, posing a significant challenge to learning a generative model that produces both high-quality and diverse molecules under limited supervision. To address this low-data drug generation issue, we propose a novel molecule generative domain adaptation paradigm (Mol-GenDA), which transfers a pre-trained GAN on a large-scale drug molecule dataset to a new disease domain using only a few references. Specifically, we introduce a molecule adaptor into the GAN generator during the fine tuning, allowing the generator to reuse prior knowledge learned in pre-training to the greatest extent and maintain the quality and diversity of the generated molecules. Comprehensive downstream experiments demonstrate that Mol-GenDA can produce high-quality and diverse drug candidates. In summary, the proposed approach offers a promising solution to expedite drug discovery for new diseases, which could lead to the timely development of effective drugs to combat emerging outbreaks.

Multifunctional Nanoassembly for MRI-Trackable Dendritic Cell Dependent and Independent Photoimmunotherapy.

Immunotherapy has emerged as a triumph in the treatment of malignant cancers. Nevertheless, current immunotherapeutics are insufficient in addressing tumors characterized by tumor cells' inadequate antigenicity and the tumor microenvironment's low immunogenicity (TME). Herein, we developed a novel multifunctional nanoassembly termed FMMC through the self-assembly of indoleamine 2,3-dioxygenase 1 (IDO-1) inhibitor 1-methyl-tryptophan prodrug (FM), Ce6, and ionic manganese (Mn2+) via noncovalent interactions. The laser-ignited FMMC treatment could induce effective immunogenic cell death and activate the STING/MHC-I signaling pathway, thus deeply sculpting the tumor-intrinsic antigenicity to achieve dendritic cell (DC)-dependent and -independent T cell responses against tumors. Meanwhile, by inhibiting IDO-1, FMMC could lead to immunosuppressive TME reversion to an immunoactivated one. FMMC-based phototherapy led to the up-regulation of programmed death-ligand 1 (PD-L1), enhancing the sensitivity of tumors to anti-PD-1 therapy. Furthermore, the incorporation of Mn2+ into FMMC resulted in an augmented longitudinal relaxivity and enhanced the MRI for monitoring the growth of primary tumors and lung metastases. Collectively, the superior reprogramming performance of immunosuppressive tumor cells and TME, combined with excellent anticancer efficacy and MRI capability, made FMMC a promising immune nanosculptor for cancer theranostics.

Dual Synergistic Tumor-Specific Polymeric Nanoparticles for Efficient Chemo-Immunotherapy.

Chemo-immunotherapy has made significant progress in cancer treatment. However, the cancer cell self-defense mechanisms, including cell cycle checkpoint and programmed cell death-ligand 1 (PD-L1) upregulation, have greatly hindered the therapeutic efficacy. Herein, norcantharidin (NCTD)-platinum (Pt) codelivery nanoparticles (NC-NP) with tumor-sensitive release profiles are designed to overcome the self-defense mechanisms via synergistic chemo-immunotherapy. NC-NP remains stable under normal physiological conditions but quickly releases 1,2-diaminocyclohexane-platinum(II) (DACHPt, a parent drug of oxaliplatin) and NCTD in response to the tumor acidity. NCTD inhibits protein phosphatase 2A (PP2A) activity to relieve cell cycle arrest and downregulates the tumor PD-L1 expression to disrupt the programmed cell death-1 (PD-1)/PD-L1 interaction, synergistically enhancing Pt-based chemotherapy and immunogenic cell death-induced immunotherapy. As a result, NC-NP exhibits potent synergistic cytotoxicity and promotes T cell recruitment to generate robust antitumor immune responses. The dual synergism exhibits potent antitumor activity against orthotopic 4T1 tumors, providing a promising chemo-immunotherapy paradigm for cancer treatment.