Notes

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Polycystic liver diseases (PLDs) are inherited genetic disorders characterized by progressive development of intrahepatic, fluid-filled biliary cysts (more than ten), which constitute the main cause of morbidity and markedly affect the quality of life. Liver cysts arise in patients with autosomal dominant PLD (ADPLD) or in co-occurrence with renal cysts in patients with autosomal dominant or autosomal recessive polycystic kidney disease (ADPKD and ARPKD, respectively). Hepatic cystogenesis is a heterogeneous process, with several risk factors increasing the odds of developing larger cysts. Depending on the causative gene, PLDs can arise exclusively in the liver or in parallel with renal cysts. Current therapeutic strategies, mainly based on surgical procedures and/or chronic administration of somatostatin analogues, show modest benefits, with liver transplantation as the only potentially curative option. Increasing research has shed light on the genetic landscape of PLDs and consequent cholangiocyte abnormalities, which can pave the way for discovering new targets for therapy and the design of novel potential treatments for patients. Herein, we provide a critical and comprehensive overview of the latest advances in the field of PLDs, mainly focusing on genetics, pathobiology, risk factors and next-generation therapeutic strategies, highlighting future directions in basic, translational and clinical research.Alterations in homeobox (HOX) gene expression are involved in the progression of several cancer types including head and neck squamous cell carcinoma (HNSCC). However, regulation of the entire HOX cluster in the pathophysiology of HNSCC is still elusive. By using different comprehensive databases, we have identified the significance of differentially expressed HOX genes (DEHGs) in stage stratification and HPV status in the cancer genome atlas (TCGA)-HNSCC datasets. The genetic and epigenetic alterations, druggable genes, their associated functional pathways and their possible association with cancer hallmarks were identified. We have performed extensive analysis to identify the target genes of DEHGs driving HNSCC. The differentially expressed HOX cluster-embedded microRNAs (DEHMs) in HNSCC and their association with HOX-target genes were evaluated to construct a regulatory network of the HOX cluster in HNSCC. Our analysis identified sixteen DEHGs in HNSCC and determined their importance in stage stratification and HPV infection. We found a total of 55 HNSCC driver genes that were identified as targets of DEHGs. The involvement of DEHGs and their targets in cancer-associated signaling mechanisms have confirmed their role in pathophysiology. Further, we found that their oncogenic nature could be targeted by using the novel and approved anti-neoplastic drugs in HNSCC. Construction of the regulatory network depicted the interaction between DEHGs, DEHMs and their targets genes in HNSCC. Hence, aberrantly expressed HOX cluster genes function in a coordinated manner to drive HNSCC. It could provide a broad perspective to carry out the experimental investigation, to understand the underlying oncogenic mechanism and allow the discovery of new clinical biomarkers for HNSCC.With modern management of primary liver cancer shifting towards non-invasive diagnostics, accurate tumor classification on medical imaging is increasingly critical for disease surveillance and appropriate targeting of therapy. Recent advancements in machine learning raise the possibility of automated tools that can accelerate workflow, enhance performance, and increase the accessibility of artificial intelligence to clinical researchers. We explore the use of an automated Tree-Based Optimization Tool that leverages a genetic programming algorithm for differentiation of the two common primary liver cancers on multiphasic MRI. Manual and automated analyses were performed to select an optimal machine learning model, with an accuracy of 73-75% (95% CI 0.59-0.85), sensitivity of 70-75% (95% CI 0.48-0.89), and specificity of 71-79% (95% CI 0.52-0.90) on manual optimization, and an accuracy of 73-75% (95% CI 0.59-0.85), sensitivity of 65-75% (95% CI 0.43-0.89) and specificity of 75-79% (95% CI 0.56-0.90) for automated machine learning. SQ22536 We found that automated machine learning performance was similar to that of manual optimization, and it could classify hepatocellular carcinoma and intrahepatic cholangiocarcinoma with an sensitivity and specificity comparable to that of radiologists. However, automated machine learning performance was poor on a subset of scans that met LI-RADS criteria for LR-M. Exploration of additional feature selection and classifier methods with automated machine learning to improve performance on LR-M cases as well as prospective validation in the clinical setting are needed prior to implementation.A novel magnetic ionic liquid based periodic mesoporous organosilica supported palladium (Fe3O4@SiO2@IL-PMO/Pd) nanocomposite is synthesized, characterized and its catalytic performance is investigated in the Heck reaction. The Fe3O4@SiO2@IL-PMO/Pd nanocatalyst was characterized using FT-IR, PXRD, SEM, TEM, VSM, TG, nitrogen-sorption and EDX analyses. This nanocomposite was effectively employed as catalyst in the Heck reaction to give corresponding arylalkenes in high yield. The recovery test was performed to study the catalyst stability and durability under applied conditions.
We evaluated the prognostic value of immunotherapy-induced organ inflammation observed on
FDG PET in patients with non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICPIs).

Data from patients with IIIB/IV NSCLC included in two different prospective trials were analyzed.
FDG PET/CT exams were performed at baseline (PET
) and repeated after 7-8weeks (PET
1) and 12-16weeks (PET
2) of treatment, using iPERCIST for tumor response evaluation. The occurrence of abnormal organ
FDG uptake, deemed to be due to ICPI-related organ inflammation, was collected.

Exploratory cohort (Nice, France) PET
1 and PET
2 revealed the occurrence of at least one ICPI-induced organ inflammation in 72.8% of patients, including midgut/hindgut inflammation (33.7%), gastritis (21.7%), thyroiditis (18.5%), pneumonitis (17.4%), and other organ inflammations (9.8%). iPERCIST tumor response was associated with improved progression-free survival (p < 0.001). iPERCIST tumor response and immuno-induced gastritis assessed on PET were both associated with improved overall survival (OS) (p < 0.001 and p = 0.032). Combining these two independent variables, we built a model predicting patients' 2-year OS with a sensitivity of 80.3% and a specificity of 69.2% (AUC = 72.7). Validation cohort (Genova, Italy) Immuno-induced gastritis (19.6% of patients) was associated with improved OS (p = 0.04). The model built previously predicted 2-year OS with a sensitivity and specificity of 72.0% and 63.6% (AUC = 70.7) and 3-year OS with a sensitivity and specificity of 69.2% and 80.0% (AUC = 78.2).

Immuno-induced gastritis revealed by early interim
FDG PET in around 20% of patients with NSCLC treated with ICPI is a novel and reproducible imaging biomarker of improved OS.
Immuno-induced gastritis revealed by early interim 18FDG PET in around 20% of patients with NSCLC treated with ICPI is a novel and reproducible imaging biomarker of improved OS.With the development of commodity economy, the emergence of fake and shoddy raisin has seriously harmed the interests of consumers and enterprises. To deal with this problem, a classification method combining near-infrared spectroscopy and pattern recognition algorithms were proposed for adulterated raisins. In this study, the experiment was performed by three kinds of raisins in Xinjiang (Hongxiangfei, Manaiti, Munage). After collecting and normalizing the spectral data, we compared the spectra of three kinds of raisins. Next the principal component analysis (PCA) was preformed to compress the dimension of the spectral data, and then classification models including support vector machine (SVM), multiscale fusion convolutional neural network (MCNN) and improved AlexNet were established to identify raisins. The accuracy of SVM, MCNN, and improved AlexNet is 100%, 92.83%, and 97.78% respectively. This study proves that near-infrared spectroscopy combined with pattern recognition is feasible for the raisin inspection.The adoption of multisensor wearables presents the opportunity of longitudinal monitoring of sleep in large populations. Personalized yet device-agnostic algorithms can sidestep laborious human annotations and objectify cross-cohort comparisons. We developed and tested a heart rate-based algorithm that captures inter- and intra-individual sleep differences in free-living conditions and does not require human input. We evaluated it on four study cohorts using different research- and consumer-grade devices for over 2000 nights. Recording periods included both 24 h free-living and conventional lab-based night-only data. We compared our optimized method against polysomnography, sleep diaries and sleep periods produced through a state-of-the-art acceleration based method. Against sleep diaries, the algorithm yielded a mean squared error of 0.04-0.06 and a total sleep time (TST) deviation of [Formula see text]2.70 (± 5.74) and 12.80 (± 3.89) minutes, respectively. When evaluated with PSG lab studies, the MSE ranged between 0.06 and 0.11 yielding a time deviation between [Formula see text]29.07 and [Formula see text]55.04 minutes. These results showcase the value of this open-source, device-agnostic algorithm for the reliable inference of sleep in free-living conditions and in the absence of annotations.Cu2SnS3 (CTS) is emerging as a promising absorber for the next generation thin film solar cells (TFSC) due to its excellent optical and electronic properties, earth-abundance and eco-friendly elemental composition. In addition, CTS can be used as precursor films for the Cu2ZnSnS4 (CZTS) synthesis. The optical properties of CTS are influenced by stoichiometry, crystalline structure, secondary phases and crystallite size. Routes for obtaining CTS films with optimized properties for TFSC are still being sought. Here, the CTS thin films synthesized by magnetron sputtering on soda lime glass (SLG) using Cu and SnS2 targets in two different stacks, were studied. The SLGCuSnS2 and SLGSnS2Cu stacks were annealed in S and Sn + S atmospheres, at various temperatures. Both stacks show a polymorphic structure, and higher annealing temperatures favor the monoclinic CTS phase formation. Morphology is influenced by the stacking order since a SnS2 top layer generates several voids on the surface due to the evaporation of SnS, while a Cu top layer provides uniform and void-free surfaces. The films in the copper-capped stack annealed under Sn + S atmosphere have the best structural, morphological, compositional and optical properties, with tunable band gaps between 1.18 and 1.37 eV. Remarkably, secondary phases are present only in a very low percent ( less then  3.5%) in samples annealed at higher temperatures. This new synthesis strategy opens the way for obtaining CTS thin films for solar cell applications, that can be used also as intermediary stage for CZTS synthesis.
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