Self-Tracking to Do Less: An Autoethnography of Long COVID That Informs the Design of Pacing Technologies

Long COVID is a post-viral illness where symptoms are still experienced more than three months after an infection of COVID 19. In line with a recent shift within HCI and research on self-tracking towards first-person methodologies, I present the results of an 18-month long autoethnographic study of using a Fitbit fitness tracker whilst having long COVID. In contrast to its designed intentions, I misused my Fitbit to do less in order to pace and manage my illness. My autoethnography illustrates three modes of using fitness tracking technologies to do less and points to the new design space of technologies for reducing, rather than increasing, activity in order to manage chronic illnesses where over-exertion would lead to a worsening of symptoms. I propose that these "pacing technologies"should acknowledge the interoceptive and fluctuating nature of the user's body and support user's decision-making when managing long-term illness and maintaining quality of life. © 2023 Owner/Author.

CCTwins: A Weakly-Supervised Transformer-based Crowd Counting Method with Adaptive Scene Consistency Attention

Recently, crowd counting has attracted significant attention, particularly in the context of the COVID-19 pandemic, due to its ability to automatically provide accurate crowd numbers in images. To address the challenges of location-level labeling, several transformer-based crowd counting methods have been proposed with only count-level supervision. However, these methods directly use the transformer as an encoder without considering the uneven crowd distribution. To address this issue, we propose CCTwins, a novel transformer-based crowd counting method with only count-level supervision. Specifically, we introduce an adaptive scene consistency attention mechanism to enhance the transformer-based model Twins-SVT-L for feature extraction in crowded scenes. Additionally, we design a multi-level weakly-supervised loss function that generates estimated crowd numbers in a coarse-to-fine manner, making it more appropriate for weakly-supervised settings. Moreover, intermediate features supervised by count-level labels are utilized to fuse multi-scale features. Experimental results on four public datasets demonstrate that our proposed method outperforms the state-of-the-art weakly-supervised methods, achieving up to a 16.6% improvement in MAE and up to a 13.8% improvement in RMSE across all evaluation settings. Moreover, the proposed CCTwins obtains competitive counting performance, even when compared to the state-of-the-art fully-supervised methods. IEEE

Ultrahigh-Resolution Photon-Counting Detector CT of the Lungs: Association of Reconstruction Kernel and Slice Thickness With Image Quality.

BACKGROUND. Prior work has shown improved image quality for photon-counting detector (PCD) CT of the lungs compared with energy-integrating detector CT. A paucity of the literature has compared PCD CT of the lungs using different reconstruction parameters. OBJECTIVE. The purpose of this study is to the compare the image quality of ultra-high-resolution (UHR) PCD CT image sets of the lungs that were reconstructed using different kernels and slice thicknesses. METHODS. This retrospective study included 29 patients (17 women and 12 men; median age, 56 years) who underwent noncontrast chest CT from February 15, 2022, to March 15, 2022, by use of a commercially available PCD CT scanner. All acquisitions used UHR mode (1024 × 1024 matrix). Nine image sets were reconstructed for all combinations of three sharp kernels (BI56, BI60, and BI64) and three slice thicknesses (0.2, 0.4, and 1.0 mm). Three radiologists independently reviewed reconstructions for measures of visualization of pulmonary anatomic structures and pathologies; reader assessments were pooled. Reconstructions were compared with the clinical reference reconstruction (obtained using the BI64 kernel and a 1.0-mm slice thickness [BI641.0-mm]). RESULTS. The median difference in the number of bronchial divisions identified versus the clinical reference reconstruction was higher for reconstructions with BI640.4-mm (0.5), BI600.4-mm (0.3), BI640.2-mm (0.5), and BI600.2-mm (0.2) (all p < .05). The median bronchial wall sharpness versus the clinical reference reconstruction was higher for reconstructions with BI640.4-mm (0.3) and BI640.2-mm (0.3) and was lower for BI561.0-mm (-0.7) and BI560.4-mm (-0.3) (all p < .05). Median pulmonary fissure sharpness versus the clinical reference reconstruction was higher for reconstructions with BI640.4-mm (0.3), BI600.4-mm (0.3), BI560.4-mm (0.5), BI640.2-mm (0.5), BI600.2-mm (0.5), and BI560.2-mm (0.3) (all p < .05). Median pulmonary vessel sharpness versus the clinical reference reconstruction was lower for reconstructions with BI561.0-mm (-0.3), BI600.4-mm (-0.3), BI560.4-mm (-0.7), BI640.2-mm (-0.7), BI600.2-mm (-0.7), and BI560.2-mm (-0.7). Median lung nodule conspicuity versus the clinical reference reconstruction was lower for reconstructions with BI561.0-mm (-0.3) and BI560.4-mm (-0.3) (both p < .05). Median conspicuity of all other pathologies versus the clinical reference reconstruction was lower for reconstructions with BI561.0 mm (-0.3), BI560.4-mm (-0.3), BI640.2-mm (-0.3), BI600.2-mm (-0.3), and BI560.2-mm (-0.3). Other comparisons among reconstructions were not significant (all p > .05). CONCLUSION. Only the reconstruction using BI640.4-mm yielded improved bronchial division identification and bronchial wall and pulmonary fissure sharpness without a loss in pulmonary vessel sharpness or conspicuity of nodules or other pathologies. CLINICAL IMPACT. The findings of this study may guide protocol optimization for UHR PCD CT of the lungs.

Design of People Count System Based on Object Detection Algorithm

At present, the Covid-19 epidemic is still spreading globally. Although the domestic epidemic has been well controlled, the prevention and control of the epidemic must not be taken lightly. Being able to count the number of people in public places in real time has played a vital role in the prevention and control of the epidemic. Deep learning networks usually cannot be directly deployed on embedded devices with low computing power due to the huge amount of parameters of convolutional neural networks. This article is based on the YOLOv5 object detection algorithm and Jetson Nano embedded platform with TensorRT and C++ accelerating, it can realize the function of counting the number of people in the classroom, on the elevator entrance, and other scenes. © 2022 SPIE.

People Counting in the Times of Covid-19

Estimating the number of people within a public building with multiple entrances is an interesting problem, especially when limitations on building occupancy hold as during the Covid-19 pandemic. In this article, we illustrate the design, prototyping and assessment of an open-source distributed Cloud-IoT service that performs such a task and detects crowd formation via EdgeAI, also accounting for privacy and security concerns. The service is deployed and thoroughly assessed over a low-cost Fog infrastructure, showing an average accuracy of 94%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

People Counting in the Times of Covid-19

Estimating the number of people within a public building with multiple entrances is an interesting problem, especially when limitations on building occupancy hold as during the Covid-19 pandemic. In this article, we illustrate the design, prototyping and assessment of an open-source distributed Cloud-IoT service that performs such a task and detects crowd formation via EdgeAI, also accounting for privacy and security concerns. The service is deployed and thoroughly assessed over a low-cost Fog infrastructure, showing an average accuracy of 94%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The neo‐nationalist ascendency: further thoughts on class, value and the return of the repressed

March 2020. On the borders of EU Europe, with the Covid pandemic threatening human lives, sociality and welfare everywhere, Syrian refugees on the ‘Balkan Route', bombed out of Idlib, are being beaten in the forests with wooden clubs by Romanian border guards before they are thrown back onto Serbian territory for further humiliations.1 Romanian return migrants, fleeing the Italian and Spanish Corona lockdowns en masse, are being told over the social networks that they should never have come back, contagious as they are imagined to be and a danger for a woefully underfunded public health system for which they have not paid taxes. Further South, the Mediterranean is once again a heavily policed cemetery for migrants and refugees from the civil wars in the Middle East and North Africa – collateral damage of Western imperial delirium and hubris – as Greece is being hailed by the European President for being the ‘shield' behind which Europe can feel safe from the supposedly associated criminality. Viktor Orbàn, meanwhile, has secured his corrupt autocracy in Hungary for another indefinite stretch of years after the parliament gave him powers to singlehandedly fight the Covid pandemic and its long-run economic after-effects in the name of the Magyars and in the face of never subsiding threats from the outside to the nation. Orbàn will also continue, even more powerfully so now, to fight immigrants, gypsies, gays, feminists, cultural Marxists, NGOs, George Soros, population decline, the EU, and everyone else who might be in his way. Critique from the EU is in Budapest rejected as being ‘motivated by politics'. Vladimir Putin, too, has just been asked by the Russian parliament to stay on indefinitely in his regal position, so as to safeguard Russia's uncertain national future. Erdogan of Turkey is sure to be inspired and will not renege from his ongoing and unprecedentedly brutal crackdown on domestic dissent and ‘traitors to the nation' while his armies are in Syria and Libya. Turkish prisons will continue to overflow.All these, and manifold other events not mentioned here, are part of processes in the European East that have been continuous (as in ‘continuous history versus discontinuous history') for at least a decade, all with a surprisingly steadfast direction. They appear to be diverse, occasioned by ethnographically deeply variegated and therefore apparently contingent events. Anthropologists, professionally spellbound by local fieldwork, are easily swayed to describe them in their singularities. But that singular appearance is misleading. These and similar events are systemically rooted, interlinked, produced by an uneven bundle of global, scaled, social and historical forces (as in ‘field of forces') that cascade into and become incorporated within a variegated and therefore differentiating terrain of national political theatres and human relationships that produce the paradox of singularly surprising outcomes with uncanny family resemblances. These forces can be summarily described as the gradual unfolding of the collapse of a global regime of embedded and multi-scalar solidarity arrangements anchored in national Fordism, developmentalism and the Cold War, into an uncertain interregnum of neoliberalised Darwinian competition and rivalry on all scales, with a powerfully rising China lurking in the background. Neo-nationalism appears from within this unfolding field of forces as a contradictory bind that seeks to enact and/or re-enact, domestically and abroad, hierarchy and deservingness, including its necessary flip side, humiliation. That is one aspect of the argument I have been trying to make since the end of the nineties (for example Kalb 2000, 2002, 2004), when such forces began to stir in the sites that I was working on and living in: The Netherlands, Belgium, Austria, Hungary and Poland.That universalising argument is easily corroborated by events in the west of the continent, which paint a similarly cohesive though phenomenologically variegated picture.2 Marine Le Pen nd Matteo Salvini are still credibly threatening to democratically overthrow liberal globalist governments in France and Italy on behalf of the ‘people' and ‘the nation', and against the elites, the EU, immigrants, the left and finance capital. Dutch politicians, in the face of the global coronavirus calamity, still believe one cannot send money to Italy and the European South lest it will be spent on ‘alcohol and women'. Anonymous comments in the Dutch press on less brutal newspaper articles often echo the tone of the one that claimed that Southern countries were mere ‘dilapidated sheds … and even with our money they will never do the necessary repair work' (NRC 30 March 2020, comments on ‘Europese solidariteit is juist ook in het Nederlandse belang'). Until its impressive policy turn-around in April/May 2020 in the face of the Covid pandemic and the fast-escalating EU fragmentation amid a world of hostile and nationalist great powers, the German government did not disagree. It was Angela Merkel herself who set up the Dutch as the leaders of a newly conceived right-wing ‘frugal' flank in the EU under the historical banner of the Hanseatic League to face down the federalist and redistributionist South. That Hanseatic banner suggested that penny-counting, competitive mercantilism and austerity, and its practical corollary, an imposed hierarchy of ‘merit' and ‘successfulness', must hang eternally over Europe. Britain, meanwhile, has valiantly elected to leave the EU in order to ‘take back control' on behalf of what Boris Johnson imagines as the ‘brilliant British nation' (The Economist 30 January 2020). It would like to refuse any further labour migrants from the mainland, and seek a future in the global Anglosphere, beefed up by a revitalised British Commonwealth where hopefully, when it comes to ceremony, not juridical equality but imperial nostalgia and deference will rule (see Campanella and Dassu 2019).

IoT Based Intelligent System for Covid - 19 hotspot detection by CNN Crowd Density Algorithm

The worldwide health crisis is caused by the widespread of the Covid-19 virus. The virus is transmitted through droplet infection and it causes the common cold, coughing, sneezing, and also respiratory distress in the infected person and sometimes becomes fatal causing death. As the world battles against covid-19, the proposed approach can help to contain the clustering of covid hotspot areas for the treatment of over a million affected patients. Drones/ Unmanned Aerial Vehicles (UAVs) offer a great deal of support in this pandemic. As suggested in this research, they can also be used to get to remote places more quickly and efficiently than with conventional means. In the hospital's control room, there would be a person in command of the ambulance drone. For hotspot area detection, the drone would be equipped with FLIR camera and for detection and recognition of face the video transmission is used by raspberry pi camera. The detection of face is done by Haar cascade Classifier and recognition of the face with LBPH algorithm. This is used for identify the each individual's medical history or can be verified by Aadhar Card. Face recognition between still and video photos was compared, and the average accuracy of still and video images was 99.8 percent and 99.57 percent, respectively. To find the hotspot area is to use the CNN Crowd counting algorithm. If the threshold value is less than equal to 0.5 than it is hotspot area , if it is greater than 0.5 and less than equal to 0.75 than it is semi-normal area , if it is greater than 0.75 and less than equal to 1 than it is normal area. © 2022 IEEE.

Access Control Through Mask Detection and Estimation of People Capacity in Covered Premises

The health emergency due to the COVID-19 pandemic requires the search for technological and intelligent solutions that facilitate the control of biosecurity measures such as social distancing, to use of a mask, and capacity in covered spaces. This work aims to develop a prototype based on artificial vision algorithms, capable of performing the automatic mask detection and people counting who go to covered premises such as bars, restaurants, gyms, cinemas, and micro-market among others. The prototype implements SSD-MobileNet object detection and SORT tracking algorithms that work on the electronic device NVIDIA Jetson Nano, equipped with two video cameras to perform mask detection and people counting respectively, as well as speakers, for emission of audible alert messages about the use of mask and the capacity estimation within the premise and an external web server too in which people counter information is displayed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Design of a Forecasting Method for Occupancy Rates in Local Public Transport Based on Data from Automatic Passenger Counting Systems

The predominant focus on individual motorized transport is neither sustainable nor socially just. One goal of a more sustainable design of the transport sector is to encourage people to use public transport. One barrier for passengers to use public transport are heavily occupied vehicles and the uncertainty about whether an empty seat is available on the desired connection. In this paper, a model is presented that is able to forecast the occupancy of vehicles in public transport. This information can be provided to passengers to increase customer satisfaction. Different sub models are presented, which differ according to their forecast horizon and the data sources used. The most important data source is data from automatic passenger counting systems collected in vehicles in the region of Northern Hesse during the project period of the research project U-hoch-3. After linking further data sources such as weather and timetable data, stratification characteristics are developed based on which occupancy states can be derived for future journeys. By linking the data with real-time data, the forecast quality can be significantly improved. It is shown which influences the Covid-19 pandemic and the introduction of the 9 € ticket in Germany had on the model development and by which functions these changes in demand can be correctly represented by the model. The results presented in this paper show that it is possible to reliably predict occupancy rates for vehicles in public transport. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A high-speed unsupervised hardware architecture for rapid diagnosis of COVID-19

In the diagnosis of COVID-19, investigation, analysis, and automatic counting of blood cell clusters are the most essential steps. Currently employed methods for cell segmentation, identification, and counting are time-consuming and sometimes performed manually from sampled blood smears, which is hard and needs the support of an expert laboratory technician. The conventional method for the blood-count-test is by automatic hematology analyzer which is quite expensive and slow. Moreover, most of the unsupervised learning techniques currently available presume the medical practitioner to have a prior knowledge regarding the number and action of possible segments within the image before applying recognition. This assumption fails most often as the severity of the disease gets increased like the advanced stages of COVID-19, lung cancer etc. In this manuscript, a simplified automatic histopathological image analysis technique and its hardware architecture suited for blind segmentation, cell counting, and retrieving the cell parameters like radii, area, and perimeter has been identified not only to speed up but also to ease the process of diagnosis as well as prognosis of COVID-19. This is achieved by combining three algorithms: the K-means algorithm, a novel statistical analysis technique-HIST (histogram separation technique), and an islanding method an improved version of CCA algorithm/blob detection technique. The proposed method is applied to 15 chronic respiratory disease cases of COVID-19 taken from high profile hospital databases. The output in terms of quantitative parameters like PSNR, SSIM, and qualitative analysis clearly reveals the usefulness of this technique in quick cytological evaluation. The proposed high-speed and low-cost architecture gives promising results in terms of performance of 190 MHz clock frequency, which is two times faster than its software implementation. © 2022 John Wiley & Sons Ltd.

Cause of death: femicide

Labelled ‘the shadow pandemic' by UN Women, violence against women received considerable global public attention during 2020–21. Underpinning this moment of public concern, there lies a substantial history of efforts to document the nature of, and campaign against, the extent of violence against women globally. This is also the case in relation to femicide. Whilst we recognise that this is a contested term, for the purposes of this paper we use femicide to refer to the killing of women and girls because they are female by male violence. Femicide, as a death to be specifically counted in law only exists in a small number of jurisdictions. Where it is so recognised, primarily in South American countries as feminicidio, such deaths represent only the tip of the iceberg of such killings globally. This paper, in drawing on empirical data from a range of different sources (including administrative data, media analysis, and Femicide Observatory data) gathered throughout 2020, considers: what it means to call a death femicide, what implications might follow if all the deaths of women at the hands of men were counted as femicide, and the extent to which extraordinary times have any bearing on this kind of ordinary death. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

A new YOLO-based method for real-time crowd detection from video and performance analysis of YOLO models.

As seen in the COVID-19 pandemic, one of the most important measures is physical distance in viruses transmitted from person to person. According to the World Health Organization (WHO), it is mandatory to have a limited number of people in indoor spaces. Depending on the size of the indoors, the number of persons that can fit in that area varies. Then, the size of the indoor area should be measured and the maximum number of people should be calculated accordingly. Computers can be used to ensure the correct application of the capacity rule in indoors monitored by cameras. In this study, a method is proposed to measure the size of a prespecified region in the video and count the people there in real time. According to this method: (1) predetermining the borders of a region on the video, (2) identification and counting of people in this specified region, (3) it is aimed to estimate the size of the specified area and to find the maximum number of people it can take. For this purpose, the You Only Look Once (YOLO) object detection model was used. In addition, Microsoft COCO dataset pre-trained weights were used to identify and label persons. YOLO models were tested separately in the proposed method and their performances were analyzed. Mean average precision (mAP), frame per second (fps), and accuracy rate metrics were found for the detection of persons in the specified region. While the YOLO v3 model achieved the highest value in accuracy rate and mAP (both 0.50 and 0.75) metrics, the YOLO v5s model achieved the highest fps rate among non-Tiny models.

Analysis of Bacteriophage Behavior of a Human RNA Virus, SARS-CoV-2, through the Integrated Approach of Immunofluorescence Microscopy, Proteomics and D-Amino Acid Quantification.

SARS-CoV-2, one of the human RNA viruses, is widely studied around the world. Significant efforts have been made to understand its molecular mechanisms of action and how it interacts with epithelial cells and the human microbiome since it has also been observed in gut microbiome bacteria. Many studies emphasize the importance of surface immunity and also that the mucosal system is critical in the interaction of the pathogen with the cells of the oral, nasal, pharyngeal, and intestinal epithelium. Recent studies have shown how bacteria in the human gut microbiome produce toxins capable of altering the classical mechanisms of interaction of viruses with surface cells. This paper presents a simple approach to highlight the initial behavior of a novel pathogen, SARS-CoV-2, on the human microbiome. The immunofluorescence microscopy technique can be combined with spectral counting performed at mass spectrometry of viral peptides in bacterial cultures, along with identification of the presence of D-amino acids within viral peptides in bacterial cultures and in patients' blood. This approach makes it possible to establish the possible expression or increase of viral RNA viruses in general and SARS-CoV-2, as discussed in this study, and to determine whether or not the microbiome is involved in the pathogenetic mechanisms of the viruses. This novel combined approach can provide information more rapidly, avoiding the biases of virological diagnosis and identifying whether a virus can interact with, bind to, and infect bacteria and epithelial cells. Understanding whether some viruses have bacteriophagic behavior allows vaccine therapies to be focused either toward certain toxins produced by bacteria in the microbiome or toward finding inert or symbiotic viral mutations with the human microbiome. This new knowledge opens a scenario on a possible future vaccine: the probiotics vaccine, engineered with the right resistance to viruses that attach to both the epithelium human surface and gut microbiome bacteria.

AI-based traffic counting: A Case Study in Vietnam

Ho Chi Minh City, particularly Vietnamese cities in general, is so busy and crowded since tremendous numbers of motorbikes move on roads. Ho Chi Minh City leaders have encountered several challenges in fully understanding and effectively dealing with problems of urban traffic for the past few decades. Software-based solutions are proper and dramatically necessary, currently. This paper presents the deployment of an AI-based application at the Ho Chi Minh City Department of Transportation. The paper mainly concentrates on traffic counting problems during the outbreak of the Covid-19 pandemic from June 2021. The performance of the AI-based application was compared with medical declaration data and achieved an accuracy of 93.80%. © 2022 IEEE.

A Retrospective Observational Study to Assess the Effect of the COVID-19 Pandemic on Spontaneous and Voluntary Abortivity in the Apulia Region of Italy.

The spread of COVID-19 in Italy required urgent restrictive measures that led to delays in access to care and to hospital overloads and impacts on the quality of services provided by the national health service. It is likely that the area related to maternal and child health was also affected. The objective of the study was to evaluate the intensity of a possible variation in spontaneous abortion (SA) and voluntary termination of pregnancy (VTP) rates in relation to the different restrictive public health measures adopted during the pandemic period of 2020. The analysis concerned the data collected on the SAs and VTPs from public and private structures in Apulia that related to the years 2019 and 2020. The SRR (standardized rate ratio) between the standardized rates by age group in 2019 and those in 2020 were calculated using a multivariable Poisson model, and it was applied to evaluate the effect of public health restrictions on the number of SAs and VTPs, considering other possible confounding factors. The SSR was significantly lower in the first months of the pandemic compared to the same period of the previous year, both for SAs and for VTPs. The major decrease in SAs and VTPs occurred during the total lockdown phase. The results, therefore, highlight how the measures to reduce infection risk could also have modified the demand for assistance related to pregnancy interruption.

Cascaded parallel crowd counting network with multi-resolution collaborative representation.

Accurately estimating the size and density distribution of a crowd from images is of great importance to public safety and crowd management during the COVID-19 pandemic, but it is very challenging as it is affected by many complex factors, including perspective distortion and background noise information. In this paper, we propose a novel multi-resolution collaborative representation framework called the cascaded parallel network (CP-Net), consisting of three parallel scale-specific branches connected in a cascading mode. In the framework, the three cascaded multi-resolution branches efficiently capture multi-scale features through their specific receptive fields. Additionally, multi-level feature fusion and information filtering are performed continuously on each branch to resist noise interference and perspective distortion. Moreover, we design an information exchange module across independent branches to refine the features extracted by each specific branch and deal with perspective distortion by using complementary information of multiple resolutions. To further improve the robustness of the network to scale variance and generate high-quality density maps, we construct a multi-receptive field fusion module to aggregate multi-scale features more comprehensively. The performance of our proposed CP-Net is verified on the challenging counting datasets (UCF_CC_50, UCF-QNRF, Shanghai Tech A&B, and WorldExpo'10), and the experimental results demonstrate the superiority of the proposed method.

SOC-V-10 Hearing loss: side-effects of multiple compounds and formulations which can be monitored with functional and histological read-outs

Purpose: Hearing loss is a major global health issue affecting around 1.5 billion people worldwide, with an increasing prevalence. Acquired hearing loss is attributed to different environmental factors including ageing, noise exposure, and the intake of ototoxic medicines. Some commonly used medications can considerably affect the auditory system, resulting in cochlear and central damage that can lead to permanent hearing loss. More than 600 classes of medications are ototoxic. The most used in clinical practice are chemotherapeutics (cisplatin) and aminoglycoside antibiotics (such as gentamicin). Some investigated drugs for Covid-19 treatment (hydroxychloroquine, HCQ) and certain drug delivery agents like cyclodextrins (CD) have also been reported to induce auditory side effects. The aim of these in vivo studies was to provide functional and histological data on auditory assessments related to cisplatin, gentamicin, HCQ, and CD, when administered similarly to clinical protocol. Method(s): The studies were conducted in Wistar rats and Albino guinea pigs: * Cisplatin was administered by intraperitoneal route at 10 mg/kg in rats * Gentamicin was administered by intramuscular route at 160 mg/kg for 5 days in rats * HCQ was administered at 62 mg/kg per os daily for five days in rats * A cyclodextrin-based formulation was administered by transtym-panic route at 4 mg/mL 1 h and 30 h after noise exposure in guinea pigs Hearing was assessed using the techniques of Distortion Product Otoacoustic Emissions (DPOAE) and Auditory Brainstem Responses (ABR) at several timepoints. DPOAE are acoustic signals created and amplified by the cochlear epithelium and measured in the ear canal. DPOAE reflect the activity of outer hair cells (OHC). ABR is an electrophysiological measure of the sensorineural activity of the auditory pathway from the cochlea to the central auditory structures in response to a sound stimulus, recorded as electric potentials from scalp electrodes. A cochleogram, an FDA-recommended histological analysis for hair cell counting, was performed at the end of certain studies. Result(s): Results based on ABR thresholds, DPOAE amplitudes, and the cochleogram, showed different patterns of auditory side-effects. Cisplatin induced immediate and permanent hearing loss;gentamicin displayed delayed side-effects on auditory measures;HCQ did not affect Outer Hair Cells but might have had an effect on neurons. CD had an immediate and prolonged effect on hearing. Conclusion(s): This short presentation will help you learn the current available methods to measure hearing in preclinical in-vivo trials using two complementary functional read-outs and a histological analysis, and to determine the different sites of damage. Copyright © 2022 Elsevier B.V.

Cause of death: femicide

Labelled ‘the shadow pandemic' by UN Women, violence against women received considerable global public attention during 2020–21. Underpinning this moment of public concern, there lies a substantial history of efforts to document the nature of, and campaign against, the extent of violence against women globally. This is also the case in relation to femicide. Whilst we recognise that this is a contested term, for the purposes of this paper we use femicide to refer to the killing of women and girls because they are female by male violence. Femicide, as a death to be specifically counted in law only exists in a small number of jurisdictions. Where it is so recognised, primarily in South American countries as feminicidio, such deaths represent only the tip of the iceberg of such killings globally. This paper, in drawing on empirical data from a range of different sources (including administrative data, media analysis, and Femicide Observatory data) gathered throughout 2020, considers: what it means to call a death femicide, what implications might follow if all the deaths of women at the hands of men were counted as femicide, and the extent to which extraordinary times have any bearing on this kind of ordinary death. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

DCBC_DeepL: Detection and Counting of Blood Cells Employing Deep Learning and YOLOv5 Model

Blood cell identification and counting is critical for doctors and physicians nowadays in order to diagnose and treat a variety of disorders. Platelet identification and counting are frequently performed in the context of many types of sickness such as COVID-19 and others. However, it is frequently costly and time intensive. Additionally, it is not widely available. From this vantage point, it is necessary to develop an efficient technical model capable of detecting and counting three fundamental types of blood cells: platelets, red blood cells, and white blood cells. Thus, this study proposes a deep learning-based model based on the YOLOv5 model with a precision of 0.799. The model consists of thre different layers such as backbone, neck and output layer The model is extremely capable of detecting and counting individual blood cells. Doctors, physicians, and other professionals will be able to detect and count blood cells using real-time images. It will significantly minimise the cost and time associated with detecting and counting blood cells by utilizing real-time blood images. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.