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Obsessive-compulsive disorder (OCD) is believed to follow a waxing and waning course, often according to environmental stressors. During the COVID-19 pandemic, pre-existing OCD symptoms were reported to increase and to change from checking to washing behaviors, while new-onset symptoms were predominantly of the hoarding type. In the present study, we followed the evolution of OCD symptoms, anxiety, depression, and insights of illness in forty-six OCD patients throughout the pandemic. Clinical measures were collected at four different time points before and during the COVID-19 pandemic in Italy. Within-subject comparisons were used to compare clinical scale scores across time, and correlations were examined between patients' baseline characteristics and changes in clinical scores. We found that all clinical measures increased during the first Italian lockdown with respect to the pre-pandemic values. Anxiety decreased during the temporary elimination of restriction provisions, whereas the severity of OCD symptoms and insight returned to pre-pandemic values during the second mandatory lockdown. These results were observed only in two sub-groups of patients: those taking benzodiazepines and those with shorter illness duration. Our findings suggest the need for additional clinical attention to these specific sub-groups of OCD patients in case of particularly distressing circumstances while pointing to a possible adaptive role of their OCD symptoms when the environment requires a higher care of hygiene and an extraordinary supply of essential resources.

An innovative mobile lidar device, developed to monitor volcanic plumes during explosive eruptions at Mt. Etna (Italy) and to analyse the optical properties of volcanic particles, was upgraded in October 2023 with the aim of improving volcanic plume retrievals. The new configuration of the lidar allows it to obtain new data on both the optical and the microphysical properties of the atmospheric aerosol. In fact, after the upgrade, the lidar is able to measure three backscattering coefficients, two extinction coefficients and two depolarisation ratios in a configuration defined as "state-of-the-art lidar", where properties such as particle size distribution and the refractive index can be derived. During the lidar implementation, we were able to test the system's performance through specific calibration measurements. A comparison in an aerosol-free region (7.2-12 km) between lidar signals at 1064 nm, 532 nm and 355 nm and the corresponding pure molecular profiles showed a relative difference of <1% between them for all the wavelengths, highlighting the good dynamic of the signals. The overlap correction allowed us to reduce the underestimation of the backscattering coefficient from 50% to 10% below 450 m and 750 m at both 355 and 532 nm, respectively. The correct alignment between the laser beam and the receiver optical chain was tested using the signal received from the different quadrants of the telescope, and the relative differences between the four directions were comparable to zero, within the margin of error. Finally, the first measurement results are shown and compared with results obtained by other instruments, with the aim of proving the ability of the upgraded system to more precisely characterise aerosol optical and microphysical properties.

In recent years, the loop-mediated isothermal amplification (LAMP) technique, designed for microbial pathogen detection, has acquired fundamental importance in the biomedical field, providing rapid and precise responses. However, it still has some drawbacks, mainly due to the need for a thermostatic block, necessary to reach 63 °C, which is the BstI DNA polymerase working temperature. Here, we report the identification and characterization of the DNA polymerase I Large Fragment from Deinococcus radiodurans (DraLF-PolI) that functions at room temperature and is resistant to various environmental stress conditions. We demonstrated that DraLF-PolI displays efficient catalytic activity over a wide range of temperatures and pH, maintains its activity even after storage under various stress conditions, including desiccation, and retains its strand-displacement activity required for isothermal amplification technology. All of these characteristics make DraLF-PolI an excellent candidate for a cutting-edge room-temperature LAMP that promises to be very useful for the rapid and simple detection of pathogens at the point of care.

Aims: The utilization of remote monitoring platforms was recommended amidst the COVID-19 pandemic. The HeartLogic index combines multiple implantable cardioverter defibrillator (ICD) sensors and has proved to be a predictor of impending heart failure (HF) decompensation. We examined how multiple ICD sensors behave in the periods of anticipated restrictions pertaining to physical activity., Methods: The HeartLogic feature was active in 349 ICD and cardiac resynchronization therapy ICD patients at 20 Italian centers. The period from 1 January to 19 July 2020, was divided into three phases: pre-lockdown (weeks 1-11), lockdown (weeks 12-20), post-lockdown (weeks 21-29)., Results: Immediately after the implementation of stay-at-home orders (week 12), we observed a significant drop in median activity level whereas there was no difference in the other contributing parameters. The median composite HeartLogic index increased at the end of the Lockdown. The weekly rate of alerts was significantly higher during the lockdown (1.56 alerts/week/100 pts, 95%CI: 1.15-2.06; IRR = 1.71, p = 0.014) and post-lockdown (1.37 alerts/week/100 pts, 95%CI: 0.99-1.84; IRR = 1.50, p = 0.072) than that reported in pre-lockdown (0.91 alerts/week/100 pts, 95%CI: 0.64-1.27). However, the median duration of alert state and the maximum index value did not change among phases, as well as the proportion of alerts followed by clinical actions at the centers and the proportion of alerts fully managed remotely., Conclusions: During the lockdown, the system detected a significant drop in the median activity level and generated a higher rate of alerts suggestive of worsening of the HF status.

Diffuse intrinsic pontine glioma (DIPG) is an incurable paediatric malignancy. Identifying the molecular drivers of DIPG progression is of the utmost importance. Long non-coding RNAs (lncRNAs) represent a large family of disease- and tissue-specific transcripts, whose functions have not yet been elucidated in DIPG. Herein, we studied the oncogenic role of the development-associated H19 lncRNA in DIPG. Bioinformatic analyses of clinical datasets were used to measure the expression of H19 lncRNA in paediatric high-grade gliomas (pedHGGs). The expression and sub-cellular location of H19 lncRNA were validated in DIPG cell lines. Locked nucleic acid antisense oligonucleotides were designed to test the function of H19 in DIPG cells. We found that H19 expression was higher in DIPG vs. normal brain tissue and other pedHGGs. H19 knockdown resulted in decreased cell proliferation and survival in DIPG cells. Mechanistically, H19 buffers let-7 microRNAs, resulting in the up-regulation of oncogenic let-7 target (e.g., SULF2 and OSMR ). H19 is the first functionally characterized lncRNA in DIPG and a promising therapeutic candidate for treating this incurable cancer.

Crystallinity is an essential indicator for evaluating the quality of fiber materials. Terahertz spectroscopy technology has excellent penetrability, no harmful substances, and commendable detection capability of absorption characteristics. The terahertz spectroscopy technology has great application potential in the field of fiber material research, especially for the characterization of the crystallinity of cellulose. In this work, the absorption peak of wood cellulose, microcrystalline cellulose, wood nano cellulose, and cotton nano cellulose were probed in the terahertz band to calculate the crystallinity, and the result compared with XRD and FT-IR analysis. The vibration model of cellulose molecular motion was obtained by density functional theory. The results showed that the average length of wood cellulose (WC) single fiber was 300 μm. The microcrystalline cellulose (MCC) was bar-like, and the average length was 20 μm. The cotton cellulose nanofiber (C-CNF) was a single fibrous substance with a length of 50 μm, while the wood cellulose nanofiber (W-CNF) was with a length of 250 μm. The crystallinity of cellulose samples in THz was calculated as follows: 73% for WC, 78% for MCC, 85% for W-CNF, and 90% for C-CNF. The crystallinity values were obtained by the three methods which were different to some extent. The absorption peak of the terahertz spectra was most obvious when the samples thickness was 1 mm and mixed mass ratio of the polyethylene and cellulose was 1:1. The degree of crystallinity was proportional to the terahertz absorption coefficients of cellulose, the five-movement models of cellulose molecules corresponded to the five absorption peak positions of cellulose.

Malignant melanoma is the deadliest form of skin cancer and, in recent years, is rapidly growing in terms of the incidence worldwide rate. The most effective approach to targeted treatment is early diagnosis. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for the image analysis and representation. They optimize the features design task, essential for an automatic approach on different types of images, including medical. In this paper, we adopted pretrained deep convolutional neural networks architectures for the image representation with purpose to predict skin lesion melanoma. Firstly, we applied a transfer learning approach to extract image features. Secondly, we adopted the transferred learning features inside an ensemble classification context. Specifically, the framework trains individual classifiers on balanced subspaces and combines the provided predictions through statistical measures. Experimental phase on datasets of skin lesion images is performed and results obtained show the effectiveness of the proposed approach with respect to state-of-the-art competitors.