East Asian summer monsoon delivers large abundances of very-short-lived organic chlorine substances to the lower stratosphere.

Deep convection in the Asian summer monsoon is a significant transport process for lifting pollutants from the planetary boundary layer to the tropopause level. This process enables efficient injection into the stratosphere of reactive species such as chlorinated very-short-lived substances (Cl-VSLSs) that deplete ozone. Past studies of convective transport associated with the Asian summer monsoon have focused mostly on the south Asian summer monsoon. Airborne observations reported in this work identify the East Asian summer monsoon convection as an effective transport pathway that carried record-breaking levels of ozone-depleting Cl-VSLSs (mean organic chlorine from these VSLSs ~500 ppt) to the base of the stratosphere. These unique observations show total organic chlorine from VSLSs in the lower stratosphere over the Asian monsoon tropopause to be more than twice that previously reported over the tropical tropopause. Considering the recently observed increase in Cl-VSLS emissions and the ongoing strengthening of the East Asian summer monsoon under global warming, our results highlight that a reevaluation of the contribution of Cl-VSLS injection via the Asian monsoon to the total stratospheric chlorine budget is warranted.

Healthcare Risk Management and Monitoring of patient falls in Italian National Service. The experience of a Local Health Authority.

Falls are a widespread concern in hospitals settings. In Italy, falls are the fourth frequent damage claim type after surgical, diagnostic and therapeutic error and 90% of falls are avoidable. The first necessary action for the prevention of falls consists in identifying the possible risk factors, in relation to the characteristics of the patient and those of the environment and the structure that hosts him, in terms of safety, organization and adequacy of the process welfare. In this work we wanted to evaluate the extent, frequency and characteristics of the phenomenon of falls in the population hospitalized at the Local Health Authority called "Roma 2", with the aim of analyzing the critical issues to allow the identification of possible preventive and improvement interventions as well as reducing the risk of falls.

Agreement between Optoelectronic System and Wearable Sensors for the Evaluation of Gait Spatiotemporal Parameters in Progressive Supranuclear Palsy.

The use of wearable sensors for calculating gait parameters has become increasingly popular as an alternative to optoelectronic systems, currently recognized as the gold standard. The objective of the study was to evaluate the agreement between the wearable Opal system and the optoelectronic BTS SMART DX system for assessing spatiotemporal gait parameters. Fifteen subjects with progressive supranuclear palsy walked at their self-selected speed on a straight path, and six spatiotemporal parameters were compared between the two measurement systems. The agreement was carried out through paired data test, Passing Bablok regression, and Bland-Altman Analysis. The results showed a perfect agreement for speed, a very close agreement for cadence and cycle duration, while, in the other cases, Opal system either under- or over-estimated the measurement of the BTS system. Some suggestions about these misalignments are proposed in the paper, considering that Opal system is widely used in the clinical context.

Immediate loading of implants inserted through impacted teeth in the aesthetic area: A retrospective study with a mean follow-up period of 7 years.

PURPOSE: To evaluate the survival and success rate of and the incidence of complications affecting implants inserted and immediately loaded in sites where an impacted tooth was present in the maxilla. MATERIALS AND METHODS: A total of 10 patients were treated and 14 implants were inserted, 4 in healed sites and 10 in extraction sites. The implant site preparation started in the crestal bone and continued in the enamel and dentine of the impacted tooth. The radiographic depth of implant penetration into the impacted tooth, peri-implant soft tissue and hard tissue condition at the 1-year follow-up and the last follow-up appointment, marginal bone loss at the 1-year follow-up and the last follow-up appointment, and the final aesthetic result were evaluated. RESULTS: The implants healed uneventfully with an adequate hard and soft tissue response and no adverse clinical or radiographic signs or symptoms. They were in function for a minimum of 3 and a maximum of 11 years (mean 7.2 years). CONCLUSION: Although further studies with a larger sample size are required to validate this unconventional approach, it can be considered a valuable clinical option to replace teeth in an area of impaction.

Immediate Loading of 3-Unit Posterior Fixed Dental Prostheses on 3 Vs 2 Implants, Comparison and Evaluation with up to 10-Year Follow-up.

AIMS: The aim of this prospective study was to evaluate and compare the implant survival rate, marginal bone levels and prostheses failure rate, of three-unit fixed dental prostheses (FDPs) supported by three vs. two implants immediately loaded in the posterior area. MATERIAL AND METHODS: Partially edentulous patients in need of a three-unit implant supported FDP in the maxillary/mandibular posterior region were recruited and randomly split into two groups: Group 1 with three-unit FDP supported by three implants (Control); Group 2 with three-unit FDP supported by two implants (Test). Implants were inserted and immediately loaded with a temporary FDP. RESULTS: Sixty-three patients were included in the study. A total of 178 implants were placed and immediately loaded (128 maxillary/50 mandibular) to support 74 immediate provisional fixed prostheses (52 maxillary and 22 mandibular) delivered on the same day of implant insertion/placement; 30 in Group 1 and 44 in Group 2. The comparison of three vs. two implants resulted in comparable implant survival rate, marginal bone loss, and prostheses failure rate. All implants healed uneventfully with no adverse clinical and radiographical signs or symptoms except for one implant failure in Group 1 resulting in a cumulative success rate of 99,5%, 98,9% for Group 1 and 100% for Group 2 with a follow-up of 6-to-10 (mean 7 years). Once loaded, the implants remained in function from a minimum of 6 years to 10 years. CONCLUSIONS: Although more studies and larger sample sizes are needed to validate this study, the results showed no difference between the two Groups, demonstrating the potential viability of both clinical options.

Capability of Machine Learning Algorithms to Classify Safe and Unsafe Postures during Weight Lifting Tasks Using Inertial Sensors.

Occupational ergonomics aims to optimize the work environment and to enhance both productivity and worker well-being. Work-related exposure assessment, such as lifting loads, is a crucial aspect of this discipline, as it involves the evaluation of physical stressors and their impact on workers' health and safety, in order to prevent the development of musculoskeletal pathologies. In this study, we explore the feasibility of machine learning (ML) algorithms, fed with time- and frequency-domain features extracted from inertial signals (linear acceleration and angular velocity), to automatically and accurately discriminate safe and unsafe postures during weight lifting tasks. The signals were acquired by means of one inertial measurement unit (IMU) placed on the sternums of 15 subjects, and subsequently segmented to extract several time- and frequency-domain features. A supervised dataset, including the extracted features, was used to feed several ML models and to assess their prediction power. Interesting results in terms of evaluation metrics for a binary safe/unsafe posture classification were obtained with the logistic regression algorithm, which outperformed the others, with accuracy and area under the receiver operating characteristic curve values of up to 96% and 99%, respectively. This result indicates the feasibility of the proposed methodology-based on a single inertial sensor and artificial intelligence-to discriminate safe/unsafe postures associated with load lifting activities. Future investigation in a wider study population and using additional lifting scenarios could confirm the potentiality of the proposed methodology, supporting its applicability in the occupational ergonomics field.

Circuital modelling in muscle tissue impedance measurements.

Electrical impedance spectroscopy (EIS) stands as a widely employed characterization technique for studying muscular tissue in both physio/pathological conditions. This methodology commonly involves modeling tissues through equivalent electrical circuits, facilitating a correlation between electrical parameters and physiological properties. Within existing literature, diverse equivalent electrical circuits have been proposed, varying in complexity and fitting properties. However, to date, none have definitively proven to be the most suiTable for tissue impedance measurements. This study aims to outline a systematic methodology for EIS measurements and to compare the performances of three widely used electrical circuits in characterizing both physiological and pathological muscle tissue conditions. Results highlight that, for optimal fitting with electrical parameters relevant to tissue characterization, the choice of the circuit to be fitted closely hinges on the specific measurement objectives, including measurement parameters and associated physiological features. Naturally, this necessitates a balance between simplicity and fitting accuracy.

Investigation of emergency department abandonment rates using machine learning algorithms in a single centre study.

A critical problem that Emergency Departments (EDs) must address is overcrowding, as it causes extended waiting times and increased patient dissatisfaction, both of which are immediately linked to a greater number of patients who leave the ED early, without any evaluation by a healthcare provider (Leave Without Being Seen, LWBS). This has an impact on the hospital in terms of missing income from lost opportunities to offer treatment and, in general, of negative outcomes from the ED process. Consequently, healthcare managers must be able to forecast and control patients who leave the ED without being evaluated in advance. This study is a retrospective analysis of patients registered at the ED of the "San Giovanni di Dio e Ruggi d'Aragona" University Hospital of Salerno (Italy) during the years 2014-2021. The goal was firstly to analyze factors that lead to patients abandoning the ED without being examined, taking into account the features related to patient characteristics such as age, gender, arrival mode, triage color, day of week of arrival, time of arrival, waiting time for take-over and year. These factors were used as process measures to perform a correlation analysis with the LWBS status. Then, Machine Learning (ML) techniques are exploited to develop and compare several LWBS prediction algorithms, with the purpose of providing a useful support model for the administration and management of EDs in the healthcare institutions. During the examined period, 688,870 patients were registered and 39188 (5.68%) left without being seen. Of the total LWBS patients, 59.6% were male and 40.4% were female. Moreover, from the statistical analysis emerged that the parameter that most influence the abandonment rate is the waiting time for take-over. The final ML classification model achieved an Area Under the Curve (AUC) of 0.97, indicating high performance in estimating LWBS for the years considered in this study. Various patient and ED process characteristics are related to patients who LWBS. The possibility of predicting LWBS rates in advance could be a valid tool quickly identifying and addressing "bottlenecks" in the hospital organization, thereby improving efficiency.

Two-year Opioid Prescription Trends in Local Sanitary Agency Naples 3 South, Campania Region, Italy. Descriptive Analyses and AI-based Translational Perspectives.

Aims: This study delves into the two-year opioid prescription trends in the Local Sanitary Agency Naples 3 South, Campania Region, Italy. The research aims to elucidate prescribing patterns, demographics, and dosage categories within a population representing 1.7% of the national total. Perspectives on artificial intelligence research are discussed. Methods: From the original dataset, spanning from January 2022 to October 2023, we processed multiple variables including demographic data, medications, dosages, drug consumption, and administration routes. The dispensing quantity was calculated as defined daily doses (DDD). Results: The analysis reveals a conservative approach to opioid therapy. In subjects under the age of 20, prescriptions accounted for 2.1% in 2022 and declined to 1.4% in 2023. The drug combination paracetamol/codeine was the most frequently prescribed, followed by tapentadol. Approximately two-thirds of the consumption pertains to oral formulations. Transdermal formulations were 15% (fentanyl 9.8%, buprenorphine 5.1%) in 2022; and 16.6% (fentanyl 10%, buprenorphine 6.6%) in 2023. These data were confirmed by the DDD analysis. The trend analysis demonstrated a significant reduction ( p < 0.001) in the number of prescribed opioids from 2022 to 2023 in adults (40-69 years). The study of rapid-onset opioids (ROOs), drugs specifically used for breakthrough cancer pain, showed higher dosage (>267 mcg) consumption among women, whereas a lower dosage (<133 mcg) was calculated for men. Fentanyl pectin nasal spray accounted for approximately one-fifth of all ROOs. Conclusion: Despite limitations, the study provides valuable insights into prescribing practices involving an important study population. The findings underscore the need for tailored approaches to prescribing practices, recognizing the complexities of pain management in different contexts. This research can contribute to the ongoing discourse on opioid use, advocating for innovative strategies that optimize therapeutic outcomes while mitigating potential risks.

Kinematic and Kinetic Gait Features Associated With Mild Cognitive Impairment in Parkinson's Disease.

Mild cognitive impairment (MCI) and gait deficits are commonly associated with Parkinson's disease (PD). Early detection of MCI associated with Parkinson's disease (PD-MCI) and its biomarkers is critical to managing disability in PD patients, reducing caregiver burden and healthcare costs. Gait is considered a surrogate marker for cognitive decline in PD. However, gait kinematic and kinetic features in PD-MCI patients remain unknown. This study was designed to explore the difference in gait kinematics and kinetics during single-task and dual-task walking between PD patients with and without MCI. Kinematic and kinetic data of 90 PD patients were collected using 3D motion capture system. Differences in gait kinematic and kinetic gait features between groups were identified by using: first, univariate statistical analysis and then a supervised machine learning analysis. The findings of this study showed that the presence of MCI in PD patients is coupled with kinematic and kinetic deviations of gait cycle which may eventually identify two different phenotypes of the disease. Indeed, as shown by the demographical and clinical comparison between the two groups, PD-MCI patients were older and more impaired. Moreover, PD-MCI kinematic results showed that cognitive dysfunction coexists with more severe axial symptoms and an increase postural flexion. A lack of physiological distal-to-proximal shift in joint kinetics was evidenced in the PD phenotype associated with cognitive impairments.

Contrast-enhanced mammography in the management of breast architectural distortions and avoidance of unnecessary biopsies.

BACKGROUND: To assess contrast-enhanced mammography (CEM) in the management of BI-RADS3 breast architectural distortions (AD) in digital breast tomosynthesis (DBT). METHODS: We retrospectively reviewed 328 women with 332 ADs detected on DBT between 2017 and 2021 and selected those classified as BI-RADS3 receiving CEM as problem-solving. In CEM recombined images, we evaluated AD's contrast enhancement (CE) according to its presence/absence, type, and size. AD with enhancement underwent imaging-guided biopsy while AD without enhancement follow-up or biopsy if detected in high/intermediate-risk women. RESULTS: AD with enhancement were 174 (52.4%): 72 (41.4%) were malignant lesions, 102 (59.6%) false positive results: 28 (16%) B3 lesions, and 74 (42.5%) benign lesions. AD without enhancement were 158 (47.6%): 26 (16.5%) were subjected to biopsy (1 malignant and 25 benign) while the other 132 cases were sent to imaging follow-up, still negative after two years. CEM's sensitivity, specificity, positive (PPV) and negative predictive values (NPV), and accuracy were 98.63%, 60.62%, 41.38%, 99.37%, and 68.98%. The AUC determined by ROC was 0.796 (95% CI, 0.749-0.844). CONCLUSION: CEM has high sensitivity and NPV in evaluating BI-RADS3 AD and can be a complementary tool in assessing AD, avoiding unnecessary biopsies without compromising cancer detection.

MarrowCellDLD: a microfluidic method for label-free retrieval of fragile bone marrow-derived cells.

Functional bone marrow studies have focused primarily on hematopoietic progenitors, leaving limited knowledge about other fragile populations, such as bone marrow adipocytes (BMAds) and megakaryocytes. The isolation of these cells is challenging due to rupture susceptibility and large size. We introduce here a label-free cytometry microsystem, MarrowCellDLD, based on deterministic lateral displacement. MarrowCellDLD enables the isolation of large, fragile BM-derived cells based on intrinsic size properties while preserving their viability and functionality. Bone marrow adipocytes, obtained from mouse and human stromal line differentiation, as well as megakaryocytes, from primary human CD34+ hematopoietic stem and progenitor cells, were used for validation. Precise micrometer-range separation cutoffs were adapted for each cell type. Cells were sorted directly in culture media, without pre-labeling steps, and with real-time imaging for quality control. At least 106 cells were retrieved intact per sorting round. Our method outperformed two FACS instruments in purity and yield, particularly for large cell size fractions. MarrowCellDLD represents a non-destructive sorting tool for large, fragile BM-derived cells, facilitating the separation of pure populations of BMAds and megakaryocytes to further investigate their physiological and pathological roles.

Development of soft tissue asymmetry indicators to characterize aging and functional mobility.

Introduction: The aging population poses significant challenges to healthcare systems globally, necessitating a comprehensive understanding of age-related changes affecting physical function. Age-related functional decline highlights the urgency of understanding how tissue composition changes impact mobility, independence, and quality of life in older adults. Previous research has emphasized the influence of muscle quality, but the role of tissue composition asymmetry across various tissue types remains understudied. This work develops asymmetry indicators based on muscle, connective and fat tissue extracted from cross-sectional CT scans, and shows their interplay with BMI and lower extremity function among community-dwelling older adults. Methods: We used data from 3157 older adults from 71 to 98 years of age (mean: 80.06). Tissue composition asymmetry was defined by the differences between the right and left sides using CT scans and the non-Linear Trimodal Regression Analysis (NTRA) parameters. Functional mobility was measured through a 6-meter gait (Normal-GAIT and Fast-GAIT) and the Timed Up and Go (TUG) performance test. Statistical analysis included paired t-tests, polynomial fitting curves, and regression analysis to uncover relationships between tissue asymmetry, age, and functional mobility. Results: Findings revealed an increase in tissue composition asymmetry with age. Notably, muscle and connective tissue width asymmetry showed significant variation across age groups. BMI classifications and gait tasks also influenced tissue asymmetry. The Fast-GAIT task demonstrated a substantial separation in tissue asymmetry between normal and slow groups, whereas the Normal-GAIT and the TUG task did not exhibit such distinction. Muscle quality, as reflected by asymmetry indicators, appears crucial in understanding age-related changes in muscle function, while fat and connective tissue play roles in body composition and mobility. Discussion: Our study emphasizes the importance of tissue asymmetry indicators in understanding how muscle function changes with age in older individuals, demonstrating their role as risk factor and their potential employment in clinical assessment. We also identified the influence of fat and connective tissue on body composition and functional mobility. Incorporating the NTRA technology into clinical evaluations could enable personalized interventions for older adults, promoting healthier aging and maintaining physical function.

Evaluation of different machine learning algorithms for predicting the length of stay in the emergency departments: a single-centre study.

Background: Recently, crowding in emergency departments (EDs) has become a recognised critical factor impacting global public healthcare, resulting from both the rising supply/demand mismatch in medical services and the paucity of hospital beds available in inpatients units and EDs. The length of stay in the ED (ED-LOS) has been found to be a significant indicator of ED bottlenecks. The time a patient spends in the ED is quantified by measuring the ED-LOS, which can be influenced by inefficient care processes and results in increased mortality and health expenditure. Therefore, it is critical to understand the major factors influencing the ED-LOS through forecasting tools enabling early improvements. Methods: The purpose of this work is to use a limited set of features impacting ED-LOS, both related to patient characteristics and to ED workflow, to predict it. Different factors were chosen (age, gender, triage level, time of admission, arrival mode) and analysed. Then, machine learning (ML) algorithms were employed to foresee ED-LOS. ML procedures were implemented taking into consideration a dataset of patients obtained from the ED database of the "San Giovanni di Dio e Ruggi d'Aragona" University Hospital (Salerno, Italy) from the period 2014-2019. Results: For the years considered, 496,172 admissions were evaluated and 143,641 of them (28.9%) revealed a prolonged ED-LOS. Considering the complete data (48.1% female vs. 51.9% male), 51.7% patients with prolonged ED-LOS were male and 47.3% were female. Regarding the age groups, the patients that were most affected by prolonged ED-LOS were over 64 years. The evaluation metrics of Random Forest algorithm proved to be the best; indeed, it achieved the highest accuracy (74.8%), precision (72.8%), and recall (74.8%) in predicting ED-LOS. Conclusions: Different variables, referring to patients' personal and clinical attributes and to the ED process, have a direct impact on the value of ED-LOS. The suggested prediction model has encouraging results; thus, it may be applied to anticipate and manage ED-LOS, preventing crowding and optimising effectiveness and efficiency of the ED.