Relationships between Seminal Plasma Metabolites, Semen Characteristics and Sperm Kinetics in Donkey (Equus asinus)

This study aimed to evaluate donkey seminal plasma metabolites and relate this information to the main characteristics of sperm quality. Sperm kinetics from 10 donkey stallions were analyzed with a computerized system at the time of collection (T0) and after 24 h storage at 4 °C (T24). Seminal plasma was frozen at -80 °C for subsequent proton nuclear magnetic resonance (1H NMR) spectroscopy. On three stallions, semen collection was repeated monthly for three times and sperm analysis also included mitochondrial activity and oxidative status. One stallion was azoospermic and a second semen collection was performed after one month. In the seminal plasma, 17 metabolites were identified; their levels showed numerous significant variations between the azoospermic and the normospermic individuals and grouped in well-defined clusters in a multivariate analysis. Comparing individuals with high and low sperm motility, the only discriminating metabolite was phenylalanine, whose levels were lower in the latter, as in the azoospermic individual. Phenylalanine was also the only metabolite highly correlated with all sperm kinematic parameters at T24. In conclusion, the present study has provided relevant information on the chemical characteristics of donkey semen, identified relationships between seminal metabolites, semen parameters, and sperm kinetics, and offered insights for future technological applications

Learning to Sail Dynamic Networks: The MARLIN Reinforcement Learning Framework for Congestion Control in Tactical Environments

Conventional Congestion Control (CC) algorithms,such as TCP Cubic, struggle in tactical environments as they misinterpret packet loss and fluctuating network performance as congestion symptoms. Recent efforts, including our own MARLIN, have explored the use of Reinforcement Learning (RL) for CC, but they often fall short of generalization, particularly in competitive, unstable, and unforeseen scenarios. To address these challenges, this paper proposes an RL framework that leverages an accurate and parallelizable emulation environment to reenact the conditions of a tactical network. We also introduce refined RL formulation and performance evaluation methods tailored for agents operating in such intricate scenarios. We evaluate our RL learning framework by training a MARLIN agent in conditions replicating a bottleneck link transition between a Satellite Communication (SATCOM) and an UHF Wide Band (UHF) radio link. Finally, we compared its performance in file transfer tasks against Transmission Control Protocol (TCP) Cubic and the default strategy implemented in the Mockets tactical communication middleware. The results demonstrate that the MARLIN RL agent outperforms both TCP and Mockets under different perspectives and highlight the effectiveness of specialized RL solutions in optimizing CC for tactical network environments.Comment: 6 pages, 4 figures, IEEE conferenc

Robotic Heller-Dor myotomy: 10-year monocentric experience compared with POEM

Achalasia is a rare motility disorder caused by an incomplete relaxation of the lower esophageal sphincter and loss of esophageal peristalsis. As a consequence, the bolus swallowing is hindered and the patients complain dysphagia, regurgitation, chest pain, respiratory symptoms and weight loss. Achalasia’s treatment has been varied over time, from therapies aiming to relax the lower sphincter of the esophagus, including drugs andBotox injection or mechanical dilatations, to surgical myotomy. Robotic or laparoscopic Heller-Dor procedure is considered the gold standard surgical treatment for symptomatic achalasia as it is proved to be effective and safe. As an alternative, Per-Oral Endoscopic Myotomy (POEM) was applied over the past decade, aiming to combine the same results of mini-invasive procedure to the advantages of endoscopic approach. In this study, we are going to compare the medium-long term results of mini-invasive Heller-Dor procedure, routinely performed in our Department, with those of POEM reported in literature

Zebrafish Patient-Derived Xenograft Model to Predict Treatment Outcomes of Colorectal Cancer Patients

The use of zebrafish embryos for personalized medicine has become increasingly popular. We present a co-clinical trial aiming to evaluate the use of zPDX (zebrafish Patient-Derived Xenografts) in predicting the response to chemotherapy regimens used for colorectal cancer patients. zPDXs are generated by xenografting tumor tissues in two days post-fertilization zebrafish embryos. zPDXs were exposed to chemotherapy regimens (5-FU, FOLFIRI, FOLFOX, FOLFOXIRI) for 48 h. We used a linear mixed effect model to evaluate the zPDX-specific response to treatments showing for 4/36 zPDXs (11%), a statistically significant reduction of tumor size compared to controls. We used the RECIST criteria to compare the outcome of each patient after chemotherapy with the objective response of its own zPDX model. Of the 36 patients enrolled, 8 metastatic colorectal cancer (mCRC), response rate after first-line therapy, and the zPDX chemosensitivity profile were available. Of eight mCRC patients, five achieved a partial response and three had a stable disease. In 6/8 (75%) we registered a concordance between the response of the patient and the outcomes reported in the corresponding zPDX. Our results provide evidence that the zPDX model can reflect the outcome in mCRC patients, opening a new frontier to personalized medicine

Minimal Extrathyroidal Extension in Predicting 1-Year Outcomes: A Longitudinal Multicenter Study of Low-to-Intermediate-Risk Papillary Thyroid Carcinoma (ITCO#4)

Background: The role of minimal extrathyroidal extension (mETE) as a risk factor for persistent papillary thyroid carcinoma (PTC) is still debated. The aim of this study was to assess the clinical impact of mETE as a predictor of worse initial treatment response in PTC patients and to verify the impact of radioiodine therapy after surgery in patients with mETE. Methods: We reviewed all records in the Italian Thyroid Cancer Observatory (ITCO) database and selected 2237 consecutive patients with PTC who satisfied the inclusion criteria (PTC with no lymph node metastases and at least 1 year of follow-up). For each case, we considered initial surgery, histological variant of PTC, tumor diameter, recurrence risk class according to the American Thyroid Association (ATA) risk stratification system, use of radioiodine therapy, and initial therapy response, as suggested by ATA guidelines. Results: At 1-year follow-up, 1831 patients (81.8%) had an excellent response, 296 (13.2%) had an indeterminate response, 55 (2.5%) had a biochemical incomplete response, and 55 (2.5%) had a structural incomplete response. Statistical analysis suggested that mETE (odds ratio [OR] 1.16, p=0.65), tumor size >2 cm (OR 1.45, p=0.34), aggressive PTC histology (OR 0.55, p=0.15), and age at diagnosis (OR 0.90, p=0.32) were not significant risk factors for a worse initial therapy response. When evaluating the combination of mETE, tumor size, and aggressive PTC histology, the presence of mETE with a >2 cm tumor was significantly associated with a worse outcome (OR 5.27, 95% CI, p=0.014). The role of radioiodine ablation in patients with mETE was also evaluated. When considering radioiodine treatment, propensity score-based matching was performed, and no significant differences were found between treated and non-treated patients (p=0.24). Conclusions: This study failed to show the prognostic value of mETE in predicting initial therapy response in a large cohort of PTC patients without lymph node metastases. The study suggests that the combination of tumor diameter and mETE can be used as a reliable prognostic factor for persistence and could be easily applied in clinical practice to manage PTC patients with low-to-intermediate risk of recurrent/persistent disease

Minimally invasive spleen-preserving distal pancreatectomy: real-world data from the italian national registry of minimally invasive pancreatic surgery

Aim: Minimally invasive distal pancreatectomy has become the standard of care for benign and low malignant lesions. Spleen preservation in this setting has been proposed to reduce surgical trauma and long-term sequelae. The aim of the current study is to present real-world data on indications, techniques, and outcomes of spleen-preserving distal pancreatectomy (SPDP). Methods: Patients who underwent SPDP and distal pancreatectomy with splenectomy (DPWS) were extracted from the 2019-2022 Italian National Registry for Minimally Invasive Pancreatic Surgery (IGoMIPS). Perioperative and pathological data were collected. Results: One hundred and ten patients underwent SPDP and five hundred and seventy-eight underwent DPWS. Patients undergoing SPDP were significantly younger (56 vs. 63.5 years; P < 0.001). Seventy-six percent of SPDP cases were performed in six out of thirty-four IGoMIPS centers. SPDP was performed predominantly for Neuroendocrine Tumors (43.6% vs.23.5%; P < 0.001) and for smaller lesions (T1 57.6% vs. 29.8%; P < 0.001). The conversion rate was higher in the case of DPWS (7.6% vs. 0.9%; P = 0.006), even when pancreatic cancer was ruled out (5.0% vs. 0.9%; P = 0.045). The robotic approach was most commonly used for SPDP (50.9% vs. 29.7%; P < 0.001). No difference in postoperative outcomes and length of stay was observed between the two groups, as well as between robotic and laparoscopic approaches in the SPDP group. A trend toward a lower rate of postoperative sepsis was observed after SPDP (0.9% vs. 5.2%; P = 0.056). In 84.7% of SPDP, splenic vessels were preserved (Kimura procedure) without an impact on short-term postoperative outcomes. Conclusion: In this registry analysis, SPDP was feasible and safe. The Kimura procedure was prevalent over the Warshaw procedure. The typical patient undergoing SPDP was young with a neuroendocrine tumor at an early stage. Robotic assistance was used more frequently for SPDP than for DPWS

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer's dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis

Polymorphisms in transcription factor binding sites and enhancer regions and pancreatic ductal adenocarcinoma risk

Genome-wide association studies (GWAS) are a powerful tool for detecting variants associated with complex traits and can help risk stratification and prevention strategies against pancreatic ductal adenocarcinoma (PDAC). However, the strict significance threshold commonly used makes it likely that many true risk loci are missed. Functional annotation of GWAS polymorphisms is a proven strategy to identify additional risk loci. We aimed to investigate single-nucleotide polymorphisms (SNP) in regulatory regions [transcription factor binding sites (TFBSs) and enhancers] that could change the expression profile of multiple genes they act upon and thereby modify PDAC risk. We analyzed a total of 12,636 PDAC cases and 43,443 controls from PanScan/PanC4 and the East Asian GWAS (discovery populations), and the PANDoRA consortium (replication population). We identified four associations that reached study-wide statistical significance in the overall meta-analysis: rs2472632(A) (enhancer variant, OR 1.10, 95%CI 1.06,1.13, p = 5.5 × 10−8), rs17358295(G) (enhancer variant, OR 1.16, 95%CI 1.10,1.22, p = 6.1 × 10−7), rs2232079(T) (TFBS variant, OR 0.88, 95%CI 0.83,0.93, p = 6.4 × 10−6) and rs10025845(A) (TFBS variant, OR 1.88, 95%CI 1.50,1.12, p = 1.32 × 10−5). The SNP with the most significant association, rs2472632, is located in an enhancer predicted to target the coiled-coil domain containing 34 oncogene. Our results provide new insights into genetic risk factors for PDAC by a focused analysis of polymorphisms in regulatory regions and demonstrating the usefulness of functional prioritization to identify loci associated with PDAC risk.</p

Polymorphisms in transcription factor binding sites and enhancer regions and pancreatic ductal adenocarcinoma risk

Genome-wide association studies (GWAS) are a powerful tool for detecting variants associated with complex traits and can help risk stratification and prevention strategies against pancreatic ductal adenocarcinoma (PDAC). However, the strict significance threshold commonly used makes it likely that many true risk loci are missed. Functional annotation of GWAS polymorphisms is a proven strategy to identify additional risk loci. We aimed to investigate single-nucleotide polymorphisms (SNP) in regulatory regions [transcription factor binding sites (TFBSs) and enhancers] that could change the expression profile of multiple genes they act upon and thereby modify PDAC risk. We analyzed a total of 12,636 PDAC cases and 43,443 controls from PanScan/PanC4 and the East Asian GWAS (discovery populations), and the PANDoRA consortium (replication population). We identified four associations that reached study-wide statistical significance in the overall meta-analysis: rs2472632(A) (enhancer variant, OR 1.10, 95%CI 1.06,1.13, p = 5.5 × 10−8), rs17358295(G) (enhancer variant, OR 1.16, 95%CI 1.10,1.22, p = 6.1 × 10−7), rs2232079(T) (TFBS variant, OR 0.88, 95%CI 0.83,0.93, p = 6.4 × 10−6) and rs10025845(A) (TFBS variant, OR 1.88, 95%CI 1.50,1.12, p = 1.32 × 10−5). The SNP with the most significant association, rs2472632, is located in an enhancer predicted to target the coiled-coil domain containing 34 oncogene. Our results provide new insights into genetic risk factors for PDAC by a focused analysis of polymorphisms in regulatory regions and demonstrating the usefulness of functional prioritization to identify loci associated with PDAC risk.</p