TRIANGLE operation, combined with adequate adjuvant chemotherapy, can improve the prognosis of pancreatic head cancer: A retrospective study.

BACKGROUND: The TRIANGLE operation involves the removal of all tissues within the triangle bounded by the portal vein-superior mesenteric vein, celiac axis-common hepatic artery, and superior mesenteric artery to improve patient prognosis. Although previously promising in patients with locally advanced pancreatic ductal adenocarcinoma (PDAC), data are limited regarding the long-term oncological outcomes of the TRIANGLE operation among resectable PDAC patients undergoing pancreaticoduodenectomy (PD). AIM: To evaluate the safety of the TRIANGLE operation during PD and the prognosis in patients with resectable PDAC. METHODS: This retrospective cohort study included patients who underwent PD for pancreatic head cancer between January 2017 and April 2023, with or without the TRIANGLE operation. Patients were divided into the PDTRIANGLE and PDnon-TRIANGLE groups. Surgical and survival outcomes were compared between the two groups. Adequate adjuvant chemotherapy was defined as adjuvant chemotherapy ≥ 6 months. RESULTS: The PDTRIANGLE and PDnon-TRIANGLE groups included 52 and 55 patients, respectively. There were no significant differences in the baseline characteristics or perioperative indexes between the two groups. Furthermore, the recurrence rate was lower in the PDTRIANGLE group than in the PDnon-TRIANGLE group (48.1% vs 81.8%, P < 0.001), and the local recurrence rate of PDAC decreased from 37.8% to 16.0%. Multivariate Cox regression analysis revealed that PDTRIANGLE (HR = 0.424; 95%CI: 0.256-0.702; P = 0.001), adequate adjuvant chemotherapy ≥ 6 months (HR = 0.370; 95%CI: 0.222-0.618; P < 0.001) and margin status (HR = 2.255; 95%CI: 1.252-4.064; P = 0.007) were found to be independent factors for the recurrence rate. CONCLUSION: The TRIANGLE operation is safe for PDAC patients undergoing PD. Moreover, it reduces the local recurrence rate of PDAC and may improve survival in patients who receive adequate adjuvant chemotherapy.

Advancing climate-resilient flood mitigation: Utilizing transformer-LSTM for water level forecasting at pumping stations.

Proactive management of pumping stations using artificial intelligence (AI) technology is vital for effectively mitigating the impacts of flood events caused by climate change. Accurate water level forecasts are pivotal in advancing the intelligent operation of pumping stations. This study proposed a novel Transformer-LSTM model to offer accurate multi-step-ahead forecasts of the flood storage pond (FSP) and river water levels for the Zhongshan pumping station in Taipei, Taiwan. A total of 19,647 ten-minute-based datasets of pumping operation and storm sewer, FSP, and river water levels were collected between 2014 and 2020 and further divided into training (70 %), validation (10 %), and test (20 %) datasets for model construction. The results demonstrate that the proposed model dramatically outperforms benchmark models by producing more accurate and reliable water level forecasts at 10-minute (T + 1) to 60-minute (T + 6) horizons. The proposed model effectively enhances the connections between input factors through the Transformer module and increases the connectivity across consecutive time series using the LSTM module. This study reveals interconnected dynamics among pumping operation and storm sewer, FSP, and river water levels, enhancing flood management. Understanding these dynamics is crucial for effective execution of management strategies and infrastructure revitalization against climate impacts. The Transformer-LSTM model's forecasts encourage water practices, resilience, and disaster risk reduction for extreme weather events.

Watershed groundwater level multistep ahead forecasts by fusing convolutional-based autoencoder and LSTM models.

The development of deep learning-based groundwater level forecast models can tackle the challenge of high dimensional groundwater dynamics, predict groundwater variation trends accurately, and manage groundwater resources effectively, thereby contributing to sustainable water resources management. This study proposed a novel ConvAE-LSTM model, which fused a Convolutional-based Autoencoder model (ConvAE) and a Long Short-Term Memory Neural Network model (LSTM), to provide accurate spatiotemporal groundwater level forecasts over the next three months. The HBV-light and LSTM models are chosen as benchmarks. An ensemble of point data and the corresponding derived images concerning the past (observations) and the future (forecasts from a conceptual model) of groundwater levels at 33 groundwater wells in Jhuoshuei River basin of Taiwan between 2000 and 2019 constituted the case study. The findings showcase the effectiveness of the ConvAE-LSTM model in extracting crucial features from both point and imagery datasets. This model successfully establishes spatiotemporal dependencies between regional images and groundwater level data over diverse time frames, leading to accurate multi-step-ahead forecasts of groundwater levels. Notably, the ConvAE-LSTM model exhibits a substantial improvement, with the R-squared values showing an increase of more than 18%, 22%, and 49% for the R1, R2, and R3 regions, respectively, compared to the HBV-light model. Additionally, it outperforms the LSTM model in this regard. This study represents a noteworthy milestone in environmental modeling, offering key insights for designing sustainable groundwater management strategies to ensure the long-term availability of this vital resource.

Radiomics and dosiomics for predicting radiation-induced hypothyroidism and guiding intensity-modulated radiotherapy.

To guide individualized intensity-modulated radiotherapy (IMRT), we developed and prospectively validated a multiview radiomics risk model for predicting radiation-induced hypothyroidism in patients with nasopharyngeal carcinoma. And simulated radiotherapy plans with same dose-volume-histogram (DVH) but different dose distributions were redesigned to explore the clinical application of the multiview radiomics risk model. The radiomics and dosiomics were built based on selected radiomics and dosiomics features from planning computed tomography and dose distribution, respectively. The multiview radiomics risk model that integrated radiomics, dosiomics, DVH parameters, and clinical factors had better performance than traditional normal tissue complication probability models. And multiview radiomics risk model could identify differences of patient hypothyroidism-free survival that cannot be stratified by traditional models. Besides, two redesigned simulated plans further verified the clinical application and advantage of the multiview radiomics risk model. The multiview radiomics risk model was a promising method to predict radiation-induced hypothyroidism and guide individualized IMRT.

Immunochemotherapy alone or immunochemotherapy plus subsequent locoregional radiotherapy in de novo metastatic nasopharyngeal carcinoma.

BACKGROUND: To demonstrate whether the benefit of locoregional radiotherapy in de novo metastatic nasopharyngeal carcinoma remains in the immunotherapy era and which patients can benefit from radiotherapy. MATERIALS AND METHODS: A total of 273 histopathology-confirmed de novo metastatic nasopharyngeal carcinoma was enrolled between May 2017 and October 2021 if receiving immunochemotherapy with or without subsequent intensity-modulated radiotherapy to the nasopharynx and neck. We compared the progression-free survival, overall survival, and safety between the two groups. Additionally, subgroup analysis was conducted and a scoring model was developed to identify suitable patients for radiation. RESULTS: There were 95 (34.8 %) patients with immunochemotherapy alone, and 178 (65.2 %) with immunochemotherapy plus subsequent locoregional radiotherapy. With a median follow-up time of 18 months, patients with immunochemotherapy plus subsequent radiotherapy had higher 1-year progression-free survival (80.6 % vs. 65.1 %, P < 0.001) and overall survival (98.3 % vs. 89.5 %, P = 0.001) than those with immunochemotherapy alone. The benefit was retained in multivariate analysis and propensity score-matched analysis. Mainly, it was more significant in patients with oligometastases, EBV DNA below 20,200 copies/mL, and complete or partial relapse after immunochemotherapy. The combined treatment added grade 3 or 4 anemia and radiotherapy-related toxicities. CONCLUSION: Immunochemotherapy plus subsequent locoregional radiotherapy prolonged the survival of de novo metastatic nasopharyngeal carcinoma with tolerable toxicities. A scoring model based on oligometastases, EBV DNA level, and response after immunochemotherapy could facilitate individualized management.

Artificial intelligence aided precise detection of local recurrence on MRI for nasopharyngeal carcinoma: a multicenter cohort study.

Background: MRI is the routine examination to surveil the recurrence of nasopharyngeal carcinoma, but it has relatively lower sensitivity than PET/CT. We aimed to find if artificial intelligence (AI) could be competent pre-inspector for MRI radiologists and whether AI-aided MRI could perform better or even equal to PET/CT. Methods: This multicenter study enrolled 6916 patients from five hospitals between September 2009 and October 2020. A 2.5D convolutional neural network diagnostic model and a nnU-Net contouring model were developed in the training and test cohorts and used to independently predict and visualize the recurrence of patients in the internal and external validation cohorts. We evaluated the area under the ROC curve (AUC) of AI and compared AI with MRI and PET/CT in sensitivity and specificity using the McNemar test. The prospective cohort was randomized into the AI and non-AI groups, and their sensitivity and specificity were compared using the Chi-square test. Findings: The AI model achieved AUCs of 0.92 and 0.88 in the internal and external validation cohorts, corresponding to the sensitivity of 79.5% and 74.3% and specificity of 91.0% and 92.8%. It had comparable sensitivity to MRI (e.g., 74.3% vs. 74.7%, P = 0.89) but lower sensitivity than PET/CT (77.9% vs. 92.0%, P < 0.0001) at the same individual-specificities. The AI model achieved moderate precision with a median dice similarity coefficient of 0.67. AI-aided MRI improved specificity (92.5% vs. 85.0%, P = 0.034), equaled PET/CT in the internal validation subcohort, and increased sensitivity (81.9% vs. 70.8%, P = 0.021) in the external validation subcohort. In the prospective cohort of 1248 patients, the AI group had higher sensitivity than the non-AI group (78.6% vs. 67.3%, P = 0.23), albeit nonsignificant. In future randomized controlled trials, a sample size of 3943 patients in each arm would be required to demonstrate the statistically significant difference. Interpretation: The AI model equaled MRI by expert radiologists, and AI-aided MRI by expert radiologists equaled PET/CT. A larger randomized controlled trial is warranted to demonstrate the AI's benefit sufficiently. Funding: The Sun Yat-sen University Clinical Research 5010 Program (2015020), Guangdong Basic and Applied Basic Research Foundation (2022A1515110356), and Guangzhou Science and Technology Program (2023A04J1788).

Controlled Encapsulation of Gold Nanoparticles into Zr-Metal-Organic Frameworks with Improved Detection Limitation of Volatile Organic Compounds via Surface-Enhanced Raman Scattering.

Volatile organic compounds (VOCs) have harmful effects on human health and the environment but detecting low levels of VOCs is challenging due to a lack of reliable biomarkers. However, incorporating gold nanoparticles (Au NPs) into metal-organic frameworks (MOFs) shows promise for VOC detection. In this study, we developed nanoscale Au@UiO-66 that exhibited surface-enhanced Raman scattering (SERS) activity even at very low levels of toluene vapors (down to 1.0 ppm) due to the thickness of the shell and strong π-π interactions between benzenyl-type linkers and toluene. The UiO-66 shell also increased the thermal stability of the Au NPs, preventing aggregation up to 550 °C. This development may be useful for sensitive detection of VOCs for environmental protection purposes.

The Correlation between Genotype Richness of Submerged Macrophytes and Periphyton Biomass: A Mesocosm Study Based on Five Dominant Submerged Macrophytes from Yangtze River.

Submerged macrophyte and periphyton are main primary producers which strongly interact with each other in clear water shallow lakes. In this study, the effects of genetic variation of the macrophyte species on periphyton biomass were studied in five submerged species. A two-year mesocosm study was conducted with four levels of genetic diversity (1, 4, 8 and 16 genotypes) for each submerged macrophyte, including 1600 individuals and 320 boxes in 20 mesocosms. Of the five submerged species, only Vallisneria spinulosa showed a positive correlation between its levels of genotype richness and the periphyton biomass. The correlation between genetic distance of genotypes and periphyton biomass was tested, which varied with the difference of seasons and species. In summary, we found that in freshwater mesocosms, the genetic diversity of submerged macrophytes may play a role in regulating the periphyton biomass, but the interaction between genetic diversity of macrophytes and periphyton biomass was not straightforward. This study will provide new insights into the interaction dynamics between the two primary producers in shallow lakes.

Purpura annularis telangiectodes of Majocchi: A case report.

BACKGROUND: Purpura annularis telangiectodes of Majocchi (PATM), also known as Majocchi, is a rare subclass of pigmented purpuric dermatoses. The etiology of PATM is unknown, but it seems more common in children and young women. The skin lesions are mostly symmetrical ring-shaped reddish-brown macules on the lower limbs. CASE SUMMARY: A 9-year-old girl, who has received treated in our department, presented with reddish-brown ring-shaped rash on both lower limbs that had been present for 6 mo. These lesions, red brownish annular or petaloid patches, were mostly found on ankles and lower limber, which do not fade when adding pressure and no feel of infiltration and no atrophy when touching those lesions. Pathological examination showed deposition of hemosiderin in papillary dermis. However, dermoscopy showed the pigmentation in the center as well as the lavender patches on the edge of lesion. The child was thus diagnosed with PATM. After diagnosis, we suggested the patient avoid strenuous exercise. she was given vitamin C tablets for oral and mometasone furoate cream for external use. Follow-up examinations and treatment continue to support the clinical diagnosis to date. CONCLUSION: This is the first report of investigating PATM using dermoscopy, which can differentiate PATM from other diseases due to its unique microscopic feature under dermoscopy. Although PATM is harmless, it still requires long-term follow-up. Moreover, dermoscopy technique can be applied for observation of multi-site lesions and correlated with histopathology. Thus, we believe this approach could be generalized for future diagnosis of PATM.

Deep learning-based precise prediction and early detection of radiation-induced temporal lobe injury for nasopharyngeal carcinoma.

Background: Radiotherapy is the mainstay of treatment for nasopharyngeal carcinoma. Radiation-induced temporal lobe injury (TLI) can regress or resolve in the early phase, but it is irreversible at a later stage. However, no study has proposed a risk-based follow-up schedule for its early detection. Planning evaluation is difficult when dose-volume histogram (DVH) parameters are similar and optimization is terminated. Methods: This multicenter retrospective study included 6065 patients between 2014 and 2018. A 3D ResNet-based deep learning model was developed in training and validation cohorts and independently tested using concordance index in internal and external test cohorts. Accordingly, the patients were stratified into risk groups, and the model-predicted risks were used to develop risk-based follow-up schedules. The schedule was compared with the Radiation Therapy Oncology Group (RTOG) recommendation (every 3 months during the first 2 years and every 6 months in 3-5 years). Additionally, the model was used to evaluate plans with similar DVH parameters. Findings: Our model achieved concordance indexes of 0.831, 0.818, and 0.804, respectively, which outperformed conventional prediction models (all P < 0.001). The temporal lobes in all the cohorts were stratified into three groups with discrepant TLI-free survival. Personalized follow-up schedules developed for each risk group could detect TLI 1.9 months earlier than the RTOG recommendation. According to a higher median predicted 3-year TLI-free survival (99.25% vs. 99.15%, P < 0.001), the model identified a better plan than previous models. Interpretation: The deep learning model predicted TLI more precisely. The model-determined risk-based follow-up schedule detected the TLI earlier. The planning evaluation was refined because the model identified a better plan with a lower risk of TLI. Funding: The Sun Yat-sen University Clinical Research 5010 Program (2015020), Guangdong Basic and Applied Basic Research Foundation (2022A1515110356), Medical Scientific Research Foundation of Guangdong Province (A2022367), and Guangzhou Science and Technology Program (2023A04J1788).

Develop a hybrid machine learning model for promoting microbe biomass production.

Since the cultivation condition of microbe biomass production (mycelia yield) involves a variety of factors, it's a laborious process to obtain the optimal cultivation condition of Antrodia cinnamomea (A. cinnamomea). This study proposed a hybrid machine learning approach (i.e., ANFIS-NM) to identify the potent factors and optimize the cultivation conditions of A. cinnamomea based on a 32 fractional factorial design with seven factors. The results indicate that the ANFIS-NM approach successfully identified three key factors (i.e., glucose, potato dextrose broth, and agar) and significantly boosted mycelia yield. The interpretability of ANFIS rules made the cultivation conditions visually interpretable. Subsequently, a three-factor five-level central composite design was used to probe the optimal yield. This study demonstrates the proposed hybrid machine learning approach could significantly reduce the time consumption in laboratory cultivation and increase mycelia yield that meets SDGs 7 and 12, hitting a new milestone for biomass production.

Sex bias in social deficits, neural circuits and nutrient demand in Cttnbp2 autism models.

Autism spectrum disorders caused by both genetic and environmental factors are strongly male-biased neuropsychiatric conditions. However, the mechanism underlying the sex bias of autism spectrum disorders remains elusive. Here, we use a mouse model in which the autism-linked gene Cttnbp2 is mutated to explore the potential mechanism underlying the autism sex bias. Autism-like features of Cttnbp2 mutant mice were assessed via behavioural assays. C-FOS staining identified sex-biased brain regions critical to social interaction, with their roles and connectivity then validated by chemogenetic manipulation. Proteomic and bioinformatic analyses established sex-biased molecular deficits at synapses, prompting our hypothesis that male-biased nutrient demand magnifies Cttnbp2 deficiency. Accordingly, intakes of branched-chain amino acids (BCAA) and zinc were experimentally altered to assess their effect on autism-like behaviours. Both deletion and autism-linked mutation of Cttnbp2 result in male-biased social deficits. Seven brain regions, including the infralimbic area of the medial prefrontal cortex (ILA), exhibit reduced neural activity in male mutant mice but not in females upon social stimulation. ILA activation by chemogenetic manipulation is sufficient to activate four of those brain regions susceptible to Cttnbp2 deficiency and consequently to ameliorate social deficits in male mice, implying an ILA-regulated neural circuit is critical to male-biased social deficits. Proteomics analysis reveals male-specific downregulated proteins (including SHANK2 and PSD-95, two synaptic zinc-binding proteins) and female-specific upregulated proteins (including RRAGC) linked to neuropsychiatric disorders, which are likely relevant to male-biased deficits and a female protective effect observed in Cttnbp2 mutant mice. Notably, RRAGC is an upstream regulator of mTOR that senses BCAA, suggesting that mTOR exerts a beneficial effect on females. Indeed, increased BCAA intake activates the mTOR pathway and rescues neuronal responses and social behaviours of male Cttnbp2 mutant mice. Moreover, mutant males exhibit greatly increased zinc demand to display normal social behaviours. Mice carrying an autism-linked Cttnbp2 mutation exhibit male-biased social deficits linked to specific brain regions, differential synaptic proteomes and higher demand for BCAA and zinc. We postulate that lower demand for zinc and BCAA are relevant to the female protective effect. Our study reveals a mechanism underlying sex-biased social defects and also suggests a potential therapeutic approach for autism spectrum disorders.

Dosiomics Risk Model for Predicting Radiation Induced Temporal Lobe Injury and Guiding Individual Intensity-Modulated Radiation Therapy.

PURPOSE: We aimed to assess the value of dose distribution-based dosiomics and planning computed tomography-based radiomics to predict radiation-induced temporal lobe injury (TLI) and guide individualized intensity modulated radiation therapy. METHODS AND MATERIALS: A total of 5599 nasopharyngeal carcinoma patients were enrolled, including 2503, 1072, 988, and 1036 patients in the training, validation, prospective test, and external test cohorts, respectively. The concordance index (C-index) was used to compare the performance of the radiomics and dosiomics models with that of the quantitative analyses of normal tissue effects in the clinic and Wen's models. The predicted TLI-free survival rates of redesigned simulated plans with the same dose-volume histogram but different dose distributions for same patient in a cohort of 30 randomly selected patients were compared by the Wilcoxon matched-pairs signed-rank test. RESULTS: The radiomics and dosiomics signatures were constructed based on 30 selected computed tomography features and 10 selected dose distribution features, respectively, which were important predictors of TLI-free survival (all P <.001). However, the radiomics signature had a low C-index. The dosiomics risk model combining the dosiomics signature, D1cc, and age had favorable performance, with C-index values of 0.776, 0.811, 0.805, and 0.794 in the training, validation, prospective test, and external test cohorts, respectively, which were better than those of the quantitative analyses of normal tissue effects in the clinic model and Wen's model (all P <.001). The dosiomics risk model can further distinguish patients in a same risk category divided by other models (all P <.05). Conversely, the other models were unable to separate populations classified by the dosiomics risk model (all P > .05). Two simulated plans with the same dose-volume histogram but different dose distributions had different TLI-free survival rates predicted by dosiomics risk model (all P ≤ .002). CONCLUSIONS: The dosiomics risk model was superior to traditional models in predicting the risk of TLI. This is a promising approach to precisely predict radiation-induced toxicities and guide individualized intensity modulated radiation therapy.

Benefit of [18F] FDG PET/CT in the diagnosis and salvage treatment of recurrent nasopharyngeal carcinoma.

PURPOSE: To compare PET/CT, MRI and ultrasonography in detecting recurrence of nasopharyngeal carcinoma and identify their benefit in staging, contouring and overall survival (OS). METHODS: Cohort A included 1453 patients with or without histopathology-confirmed local recurrence, while cohort B consisted of 316 patients with 606 histopathology-confirmed lymph nodes to compare the sensitivities and specificities of PET/CT, MRI and ultrasonography using McNemar test. Cohorts C and D consisted of 273 patients from cohort A and 267 patients from cohort B, respectively, to compare the distribution of PET/CT-based and MRI-based rT-stage and rN-stage and the accuracy of rN-stage using McNemar test. Cohort E included 30 random patients from cohort A to evaluate the changes in contouring with or without PET/CT by related-samples T test or Wilcoxon rank test. The OS of 61 rT3-4N0M0 patients staged by PET/CT plus MRI (cohort F) and 67 MRI-staged rT3-4N0M0 patients (cohort G) who underwent similar salvage treatment were compared by log-rank test and Cox regression. RESULTS: PET/CT had similar specificity to MRI but higher sensitivity (93.9% vs. 79.3%, P < 0.001) in detecting local recurrence. PET/CT, MRI and ultrasonography had comparable specificities, but PET/CT had greater sensitivity than MRI (90.9% vs. 67.6%, P < 0.001) and similar sensitivity to ultrasonography in diagnosing lymph nodes. According to PET/CT, more patients were staged rT3-4 (82.8% vs. 68.1%, P < 0.001) or rN + (89.9% vs. 69.3%, P < 0.001), and the rN-stage was more accurate (90.6% vs. 73.8%, P < 0.001). Accordingly, the contours of local recurrence were more precise (median Dice similarity coefficient 0.41 vs. 0.62, P < 0.001) when aided by PET/CT plus MRI. Patients staged by PET/CT plus MRI had a higher 3-year OS than patients staged by MRI alone (85.5% vs. 60.4%, P = 0.006; adjusted HR = 0.34, P = 0.005). CONCLUSION: PET/CT more accurately detected and staged recurrence of nasopharyngeal carcinoma and accordingly complemented MRI, providing benefit in contouring and OS.

Role of postoperative chemoradiotherapy in head and neck cancer without positive margins or extracapsular extension: a propensity score-matching analysis.

BACKGROUND: The aim of this work was to determine whether patients with intermediate-risk head and neck squamous cell carcinoma (HNSCC) can benefit from postoperative chemoradiotherapy (POCRT). METHODS: Patients without extracapsular extension (ECE) or positive margins (PMs) who received POCRT or postoperative radiotherapy (PORT) at our center were retrospectively (December 2009 to October 2018) included for analysis, in particular, using a propensity score-matching method. RESULTS: After matching, 264 patients were enrolled, including 142 (41.2%) patients with pT3-4, 136 (38.3%) patients with pN2-3, 68 (21.1%) patients with perineural invasion, and 45 (12.8%) patients with lymphatic/vascular space invasion. With a median follow-up of 52 months, 3-year overall survival (OS), locoregional relapse-free survival (LRFS), distant metastasis-free survival (DMFS) and disease-free survival (DFS) rates were 72.4%, 79.3%, 83.5% and 62.5%, respectively. pN2-3 was an independent risk factor for OS (p < 0.001), DFS (p < 0.001), LRFS (p < 0.001) and DMFS (p = 0.002), while pT3-4 was a poor prognostic factor for DMFS (p = 0.005). Overall, patients receiving POCRT had no significant differences from those receiving PORT in OS (p = 0.062), DFS (p = 0.288), LRFS (p = 0.076) or DMFS (p = 0.692). But notably, patients with pN2-3 achieved better outcomes from POCRT than PORT in 3-year OS (p = 0.050, 63.9% vs. 47.9%) and LRFS (p = 0.019, 74.6% vs. 54.9%). And patients with pT3-4 also had higher 3-year LRFS (p = 0.014, 88.5% vs. 69.1%) if receiving POCRT. CONCLUSIONS: Among all intermediate-risk pathological features, pN2-3 and pT3-4 were independent unfavorable prognostic factors for patients with HNSCC without PMs or ECE. POCRT can improve the survival outcomes of patients with pN2-3 or pT3-4.

Antioxidative, cytoprotective and whitening activities of fragrant pear fruits at different growth stages.

Pear is one of the most popular fruits in the world. With the fruit ripening, a series of physiological changes have taken place in fragrant pear, but up to now, the research on the metabolism and biological activity of phenolic compounds in different growth stages of fragrant pear is still lacking. In this study, four kinds of Xinjiang pears were selected as research objects, and the changes of phenolic content, antioxidant capacity, cell protection and whitening activity during fruit development were analyzed. The results showed that the phenolic content and antioxidant capacity of four pear varieties presented a decreasing trend throughout the developmental stages. The phenolic content and antioxidant activity of the four pears in the young fruit stage were the highest, and the active ingredients of the Nanguo pear were higher than the other three pear fruits. Pear extract could protect cells by eliminating excessive ROS in cells, especially in young fruit stage. The western blot results showed that the extract of fragrant pear in the young fruit stage could inhibit the expression of TYR, TYR1 and MITF in B16 cells, and it was speculated that the extract of fragrant pear in the young fruit stage might have good whitening activity. Therefore, the findings suggest that young pear display a good antioxidant potential and could have a good application prospect in food preservation and health product industry.

Dermoscopic characteristics of membranous aplasia cutis congenita: Report of 56 cases.

BACKGROUND: Membranous aplasia cutis congenita (MACC) presents at birth characterized by oval epidermis defect. Skin lesions with MACC have various clinic manifestations. In recent years, the usefulness of trichoscopy (scalp dermoscopy) has been reported for hair loss diseases. However, the dermoscopic features of MACC were mostly reported by case reports. OBJECTIVES: To summarized the obvious dermoscopic characteristics of MACC. MATERIALS & METHODS: These 56 cases met the clinical diagnostic criteria for MACC without forceps delivery complications or other birth injuries. To find the dermoscopic characteristics of MACC by summarizing 56 infants' dermoscopic pictures. RESULTS: The dermoscopic manifestation of MACC are characterized by hair follicle openings and hair deficiency in the center of skin lesions, translucent epidermis, hair root and hair bulb arranged along the margins of skin lesion. CONCLUSION: The typical dermoscopic characteristics of MACC could help clinicians to early diagnose and differential diagnosis.

Evolution of nonenzymatic browning during the simulated Msalais-production process in models of grape juice.

Msalais is a wine fermented from boiled grape juice. Nonenzymatic browning (NEB) greatly affects the quality of Msalais, but to date its mechanism has not been systematically analyzed. In the current study, the evolution of NEB during Msalais production was investigated using models of grape juice. 5-Hydroxymethylfurfural (5-HMF), browning index (BI), yellowness index (YI), sample absorbance at 420 (A420), and b* increased during heating, with a clear transition point at 110 min. The Maillard reaction (MR) was the major contributor to NEB. Vitamin C (VC) facilitated NEB in the late stage of heating. During heating that lasted over 130 min, glucose contributed to NEB more than fructose, while the reverse was true for heating lasting less than 130 min. Proline (Pro) was the most important amino acid in facilitating NEB. BI and A420 decreased during fermentation, while increasing slightly during wine storage. In conclusion, this study identified the evolution of NEB during the Msalais process, which will facilitate the control of traditional Msalais production for improved wine quality.

Phase separation and zinc-induced transition modulate synaptic distribution and association of autism-linked CTTNBP2 and SHANK3.

Many synaptic proteins form biological condensates via liquid-liquid phase separation (LLPS). Synaptopathy, a key feature of autism spectrum disorders (ASD), is likely relevant to the impaired phase separation and/or transition of ASD-linked synaptic proteins. Here, we report that LLPS and zinc-induced liquid-to-gel phase transition regulate the synaptic distribution and protein-protein interaction of cortactin-binding protein 2 (CTTNBP2), an ASD-linked protein. CTTNBP2 forms self-assembled condensates through its C-terminal intrinsically disordered region and facilitates SHANK3 co-condensation at dendritic spines. Zinc binds the N-terminal coiled-coil region of CTTNBP2, promoting higher-order assemblies. Consequently, it leads to reduce CTTNBP2 mobility and enhance the stability and synaptic retention of CTTNBP2 condensates. Moreover, ASD-linked mutations alter condensate formation and synaptic retention of CTTNBP2 and impair mouse social behaviors, which are all ameliorated by zinc supplementation. Our study suggests the relevance of condensate formation and zinc-induced phase transition to the synaptic distribution and function of ASD-linked proteins.

Deep neural networks for spatiotemporal PM2.5 forecasts based on atmospheric chemical transport model output and monitoring data.

Reliable long-horizon PM2.5 forecasts are crucial and beneficial for health protection through early warning against air pollution. However, the dynamic nature of air quality makes PM2.5 forecasts at long horizons very challenging. This study proposed a novel machine learning-based model (MCNN-BP) that fused multiple convolutional neural networks (MCNN) with a back-propagation neural network (BPNN) for making spatiotemporal PM2.5 forecasts for the next 72 h at 74 stations covering the whole Taiwan simultaneously. Model configuration involved an ensemble of massive hourly air quality and meteorological monitoring datasets and the existing publicly-available PM2.5 simulated (forecasted) datasets from an atmospheric chemical transport (ACT) model. The proposed methodology collaboratively constructed two CNNs to mine the observed data (the past) and the forecasted data from ACT (the future) separately. The results showed that the MCNN-BP model could significantly improve the accuracy of spatiotemporal PM2.5 forecasts and substantially reduce the forecast biases of the ACT model. We demonstrated that the proposed MCNN-BP model with effective feature extraction and good denoising ability could overcome the curse of dimensionality and offer satisfactory regional long-horizon PM2.5 forecasts. Moreover, the MCNN-BP model has considerably shorter computational time (5 min) and lower computational load than the compute-intensive ACT model. The proposed approach hits a milestone in multi-site and multi-horizon forecasting, which significantly contributes to early warning against regional air pollution.