Impact of Intermittent Intraoperative Neuromonitoring (IONM) on the Learning Curve for Total Thyroidectomy by Residents in General Surgery.

INTRODUCTION: Recurrent laryngeal nerve (RNL) identification constitutes the standard in thyroidectomy. Intraoperative nerve monitoring (IONM) has been introduced as a complementary tool for RLN functionality evaluation. The aim of this study is to establish how routine use of IONM can affect the learning curve (LC) in thyroidectomy. METHODS: Patients undergoing total thyroidectomy performed by surgery residents in their learning curve course in 2 academic hospitals, were divided into 2 groups: Group A, including 150 thyroidectomies performed without IONM by 3 different residents, and Group B, including 150 procedures with routine use of intermittent IONM, by other 3 different residents. LC was measured by comparing operative time (OT), its stabilization during the development of the LC, perioperative complication rate. RESULTS: As previously demonstrated, the LC was achieved after 30 procedures, in both groups, with no differences due to the use of IONM. Similarly, there were no significant differences among the 2 groups, and between subgroups independently matched, for both OT and complications, even when comparing RLN palsy. Direct nerve visualization and IONM assessment rates were comparable in all groups, and no bilateral RLN palsy (transient or permanent) were reported. No case of interrupted procedure to unilateral lobectomy, due to evidence of RLN injury, was reported. CONCLUSIONS: The study demonstrates that the use of IONM thyroid surgery, despite requiring a specific training with experienced surgeons, does not particularly affect the learning curve of residents approaching this kind of surgery, and for this reason its routine use should be encouraged even for trainees.

Cross-Omic Transcription Factor Analysis: An Insight on Transcription Factor Accessibility and Expression Correlation.

It is well known how sequencing technologies propelled cellular biology research in recent years, providing incredible insight into the basic mechanisms of cells. Single-cell RNA sequencing is at the front in this field, with single-cell ATAC sequencing supporting it and becoming more popular. In this regard, multi-modal technologies play a crucial role, allowing the possibility to simultaneously perform the mentioned sequencing modalities on the same cells. Yet, there still needs to be a clear and dedicated way to analyze these multi-modal data. One of the current methods is to calculate the Gene Activity Matrix (GAM), which summarizes the accessibility of the genes at the genomic level, to have a more direct link with the transcriptomic data. However, this concept is not well defined, and it is unclear how various accessible regions impact the expression of the genes. Moreover, the transcription process is highly regulated by the transcription factors that bind to the different DNA regions. Therefore, this work presents a continuation of the meta-analysis of Genomic-Annotated Gene Activity Matrix (GAGAM) contributions, aiming to investigate the correlation between the TF expression and motif information in the different functional genomic regions to understand the different Transcription Factors (TFs) dynamics involved in different cell types.

Neuronal Spike Shapes (NSS): A straightforward approach to investigate heterogeneity in neuronal excitability states.

The mammalian brain exhibits a remarkable diversity of neurons, contributing to its intricate architecture and functional complexity. The analysis of multimodal single-cell datasets enables the investigation of cell types and states heterogeneity. In this study, we introduce the Neuronal Spike Shapes (NSS), a straightforward approach for the exploration of excitability states of neurons based on their Action Potential (AP) waveforms. The NSS method describes the AP waveform based on a triangular representation complemented by a set of derived electrophysiological (EP) features. To support this hypothesis, we validate the proposed approach on two datasets of murine cortical neurons, focusing it on GABAergic neurons. The validation process involves a combination of NSS-based clustering analysis, features exploration, Differential Expression (DE), and Gene Ontology (GO) enrichment analysis. Results show that the NSS-based analysis captures neuronal excitability states that possess biological relevance independently of cell subtype. In particular, Neuronal Spike Shapes (NSS) captures, among others, a well-characterized fast-spiking excitability state, supported by both electrophysiological and transcriptomic validation. Gene Ontology Enrichment Analysis reveals voltage-gated potassium (K+) channels as specific markers of the identified NSS partitions. This finding strongly corroborates the biological relevance of NSS partitions as excitability states, as the expression of voltage-gated K+ channels regulates the hyperpolarization phase of the AP, being directly implicated in the regulation of neuronal excitability.

Modeling landslide activity and sediment connectivity after eruptions: Insights from the Blanco River (Chile).

Volcanic eruptions can disrupt entire river basins by affecting the hydro-geomorphic characteristics of channel networks and hillslopes. Reports suggest a pulsed and delayed increase in landslide activity following the eruptions, which, depending on the degree of linkage between hillslopes and channels, i.e. sediment connectivity, can represent a massive source of sediment input for the fluvial system. Therefore, predicting landslide occurrence and sediment connectivity is fundamental for management risk strategies, especially in such dynamic and complex environments. The aim of this work is to develop and offer a more reliable approach to map the areas susceptible to landslides and connected to the active channel in a catchment impacted by volcanic eruption. The analyses were carried out in the Blanco River catchment in southern Chile, affected by the Chaitén eruption (2008-09). A combined approach is presented, based on landslide susceptibility models, carried out multi-temporally (from 2010 to 2019), and a threshold-based sediment connectivity map. The results showed that the highest landslide occurrence was reported 4 years after the eruption, whereas the faster increase in the overall area affected was observed only after 7 years. Landslide susceptibility models showed high accuracy when applied in the same year, but were less accurate in predicting future occurrences. This result is ascribed to the dynamic conditions of the vegetation, regenerating quickly after the mass movements. Nevertheless, considering the potential sources of error, the combined landslide susceptibility-connectivity map for the year 2019 well-identified relevant areas for catchment management. The largest part of the catchment was found non-susceptible and disconnected, while areas classified as susceptible and connected represent only 3.1 %. The application of this novel approach allowed to unravel the geomorphic trajectory of the study area and, more importantly, can represent a benchmark for future applications in other catchments affected by large disturbances.

Characteristics of COVID-19 vaccinated and unvaccinated patients admitted to Careggi University Hospital, Florence, Italy.

More than 11.5 billion COVID-19 vaccine doses have been administered around the world. Although vaccine effectiveness for severe infections is reported to be 89.0%, breakthrough infections are common and may lead to severe outcome in fragile population. We conducted a real-world observational study on 420 COVID-19 admitted patients from July 2021 to January 2022 in a tertiary level Italian hospital. We collected patient's vaccination and SARS-CoV-2 serological status, SARS-CoV-2 treatments, oxygen supports, intensive (ICU) and subintensive (sub-ICU) care unit admissions, length of staying (LoS) and in-hospital mortality. One-hundred-seventy-two vaccinated and 248 unvaccinated patients were admitted during the study period. Vaccinated group (Vg) had a significantly more elevated Charlson Comorbidity Index than Unvaccinated group (UVg), and no statistical differences were found in terms of in-hospital mortality, LoS or ICU and sub-ICU admissions. Among Vg, anti-S antibodies were detected in 86.18% of patients (seropositives). Vaccinated seronegative patients' in-hospital mortality was significantly higher than vaccinated seropositive patients (33.33% vs 10.69%, p = 0.0055): in particular, mortality rate in 45-69 years old population was higher in vaccinated seronegative group, and comparable in patients ≥ 70 years old. No differences in terms of outcome were registered between Vg and UVg, taking into account that Vg was considerably older and with more comorbidities. In line with other recent observations, higher mortality rate was evidenced for seronegative vaccinated patients. Primary prophylaxis and early treatments result to be necessary, especially for older and immunosuppressed populations.

GAGAM v1.2: An Improvement on Peak Labeling and Genomic Annotated Gene Activity Matrix Construction.

Single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) is rapidly becoming a powerful technology for assessing the epigenetic landscape of thousands of cells. However, the sparsity of the resulting data poses significant challenges to their interpretability and informativeness. Different computational methods are available, proposing ways to generate significant features from accessibility data and process them to obtain meaningful results. Foremost among them is the peak calling, which interprets the raw scATAC-seq data generating the peaks as features. However, scATAC-seq data are not trivially comparable with single-cell RNA sequencing (scRNA-seq) data, an increasingly pressing challenge since the necessity of multimodal experiments integration. For this reason, this study wants to improve the concept of the Gene Activity Matrix (GAM), which links the accessibility data to the genes, by proposing an improved version of the Genomic-Annotated Gene Activity Matrix (GAGAM) concept. Specifically, this paper presents GAGAM v1.2, a new and better version of GAGAM v1.0. GAGAM aims to label the peaks and link them to the genes through functional annotation of the whole genome. Using genes as features in scATAC-seq datasets makes different datasets comparable and allows linking gene accessibility and expression. This link is crucial for gene regulation understanding and fundamental for the increasing impact of multi-omics data. Results confirm that our method performs better than the previous GAMs and shows a preliminary comparison with scRNA-seq data.

Rapid improvement of psychiatric stigmata after IFN-free treatment in HCV patients with and without cryoglobulinemic vasculitis.

OBJECTIVE: Hepatitis C virus (HCV) causes neuropsychiatric disorders and quality of life impairment, especially in patients with cryoglobulinemic vasculitis (CV). Direct acting antivirals (DAAs) are effective in most extrahepatic HCV diseases, but limited information exists regarding the outcome of psychiatric disorders in patients with and without CV, after therapy. We aimed to evaluate psychiatric outcomes, in HCV-patients with and without CV, before and after successful DAA therapy. METHODS: We prospectively studied DAA-treated HCV-patients, stratified into presence (CV) or absence of CV (NON-CV). Four psychometric scales were administered to assess depression (HAM-D and MADRS), anxiety (HAM-A), and mania (MRS). Short-Form-36 questionnaires evaluated quality of life. RESULTS: Seventy-six patients were recruited, and 47 CV and 29 NON-CV were treated with antivirals. At baseline, depression and anxiety, from mild to severe, were frequently shown, with the most advanced cases in thee CV group; no patients achieved the scores for mania. A significant improvement emerged for all the psychometric scales in the entire population and in the subgroups, after viral eradication even in the short-term outcome. The Short-Form-36 summary components showed benefits. CONCLUSIONS: After HCV eradication, the depression and anxiety scores significantly improved and severity grade generally lowered. DAA-positive effects on mental disorders should be considered part of the therapy outcome, being beneficial especially in CV patients who usually have worse baseline mental scores. Key Points • HCV frequently causes psychiatric disorders and an often-invalidating autoimmune/lymphoproliferative disease called cryoglobulinemic vasculitis. • The new direct acting antivirals (DAAs) are very effective and well tolerated by HCV-patients. • This study shows DAA-induced benefits on depression and anxiety in HCV-patients that are especially evident in CV patients who usually have worse baseline mental scores. • DAA-induced benefits are observed in the short-term post-therapy follow-up, in contrast with data previously obtained in HCV patients treated with IFN-based anti-HCV therapy.

The morphological response of the Tegnas alpine catchment (Northeast Italy) to a Large Infrequent Disturbance.

A recent storm (27th-30th October 2018), named Vaia, hit most part of the Northeast of Italy affecting the geomorphic aspect of almost all mountain catchments of the area. The event triggered new instabilities such as windthrows, landslides and debris flows. At present, few studies dealt with the analysis of the impact of a Large Infrequent Disturbance at large catchment scale. This work provides a focus on the Tegnas Torrent Basin (Belluno Province) and aims at detecting how, where, and how much this storm affected the basin. Moreover, it integrates two different approaches considering both the dynamic and static aspects of the sediment, via DEM of Difference (DoD) and Index of Connectivity (IC), respectively. The Tegnas sub-basins responded contrastingly: the Bordina (volcanic origin and covered by pastures and spruce forests) was mainly affected by windthrows (7% of the sub-basin area) and landslides (0.5%), while the Angheraz (outcropping dolomite rocks), was stricken only by debris flows (1.0%). Morphological changes were clear along the entire channel network, with predominant erosion in the steepest upstream parts (over 2 m of the channel elevation), and deposition in the lower main valley floor (over 3 m of the channel elevation). The IC analysis along the instabilities highlighted that the windthrows occurred mainly in areas of high connectivity, which may be important for future management strategies. Moreover, the proposed integrated approach, based on the combination IC-DoD, permitted a detailed identification of sediment routing and a contemporary estimation of erosion and deposition volumes generated by a high magnitude low-frequency event. Based on these results, cascading processes are expected and further analysis are required to fully consider the impact of a Large Infrequent Disturbance.

Assessing the effect of fire severity on sediment connectivity in central Chile.

Chilean territory is recurrently affected by severe wildfires, which drastically reduce the forest cover and promote runoff, soil erosion and slope instabilities. To understand how the geomorphic system responds to wildfires in terms of sediment dynamics, the assessment of sediment connectivity, i.e. the property describing the relationships between compartments of a geomorphic system, is crucial. This study aims to quantify the spatial linkages between fire severity and sediment connectivity to identify common patterns and driving factors. The compound use of field data and open-source satellite imagery helped to apply the Relative differenced Normalized Burn Ratio (RdNBR) and the Index of Connectivity (IC) in the context of two consecutive wildfires, occurred in 2002 and 2015, in the Rio Toro catchment (Chile). The fire severity assessment showed that the 2002 event affected 90% of the catchment, with high severity areas representing around 70%. The 2015 wildfire instead, affected 76% of the catchment with moderate severity around 42%. Accordingly, as result of the sudden reduction in forest cover in severely affected areas, the IC changed after both wildfires with an overall increase of 1.07 and 0.54, respectively. However, only for the second disturbance, it was possible to observe a clear relationship between the RdNBR and the IC variations. The different degree of vegetation cover heterogeneity between the two pre-wildfire scenarios contributed to different fire severity and IC variability between the two disturbances. The use of open-source satellite data and the development of a weighting factor (W), to be used in IC and able to capture the land cover change driven by the wildfires, could make the application of this approach straightforward, promoting its reproducibility in other catchments for land management and risk mitigation purposes.

Sediment connectivity changes in an Andean catchment affected by volcanic eruption.

River systems are continuously affected by multiple natural and anthropogenic phenomena. Among major natural disturbances, volcanic eruptions have the capability to overthrow the forms and to modify dramatically the morphological setting and sediment connectivity of the system. Moreover, the input of sediments in the channel network can lead to active channel widening, bed aggradation and river scouring, which in turn may affect downstream human settlements. In this context, the present research aims to investigate sediment connectivity by means of a geomorphometric index in a catchment affected by an explosive volcanic eruption in order to assess its variation before and after the disturbance. We developed a workflow exploiting the use of open source data (i.e., Global Digital Elevation Models, satellite imagery) to represent the impedance to the sediment fluxes and to apply the Index of Connectivity (IC) in this context characterized by the lack of high-resolution topographic data. The study area is the Blanco River basin, southern Chile, which was heavily affected by the eruption of Chaitén volcano (2008-2009) that caused the partial destruction of the forest and the profound alteration of Blanco River's morphology. The application of the IC on different land cover scenarios, derived by combining field observations and satellite image classification techniques, showed an increase in sediment connectivity after the volcanic eruption. In addition, the results highlighted different patterns of connectivity according to the expansion of the active channel induced by the massive input of pyroclastic material. The approach proposed in this study case offers a methodology to investigate sediment connectivity in a river catchment affected by natural disturbance where high-resolution data are not available. The results of the study help to improve knowledge on the effect of volcanic eruptions in the hydrologic catchment and to improve watershed management strategies in such kind of environment.