Process of Posthospital Care Involving Telemedicine Solutions for Patients after Total Hip Arthroplasty

The importance of telemedicine technologies around the world has been growing for many years, and it turned out to be a particularly important issue for conducting some medical procedures during the SARS-CoV-2 pandemic. It is necessary to create interdisciplinary teams to design and implement improved procedures using telemedicine tools. The aim of the article is to develop original, improved posthospital patient care process after total hip arthroplasty (THA) with the use of telemedicine technologies. In the study, a literature review and empirical research were used. The conducted research resulted in the designing an original posthospital patient care process after THA that uses telematics technologies. Due to the use of analyzed telemedicine technologies, the designed patient care process brings a possibility to increase the patient′s safety by monitoring life parameters, allowing for regular, remote contact with specialists and to be supervised remotely. All this may contribute to shortening the convalescence time, reducing the risk of complications, as well as reducing treatment costs. The designed model is ready for further clinical research with the participation of medical staff, patients after THA and patient caregivers.

Review of physical methods in the treatment of Bell’s palsy

Peripheral facial nerve damage is the most common condition of all cranial nerve damage; this nerve innervates the face in motor, sensory and parasympathetic ways. Bell’s palsy prevents patients from functioning normally by disturbing the speech function, eating and facial expressions. It can also lead to severe infl ammation of the eyeball and consequently to blindness. The study focuses on the influence of appropriately selected physical procedures on the time over which facial palsy affects a patient. The use of therapeutic methods such as: electrotherapy, heat treatment, facial massage, kinesitherapy and fi llers were also presented Comprehensive rehabilitation therapy based on the cooperation of doctors, physiotherapists and cosmetologists signifi cantly improves the results of pharmacological treatment and shortens convalescence time.

Numerical Simulations of a Modified SIR Model Fitting Statistical Datafor COVID19

We consider a system of ordinary differential equations obtained by modifying the classical SIR modelin epidemiology in order to account for the particular features of COVID­19 and the structure of the availablestatistical data. Its main feature is that the infectious state is being split in two different stages. In the first one,which lasts a few days after being infected, the individuals are considered to be contagious and able to spreadfurther the disease. After this, the individuals are considered to be isolated and this second stage lasts until eitherrecovery or death is reported. The parameters of the model are fitted for several countries (Germany, Italy, Spain,Russia, USA, Romania) such that the solution matches the known number of new cases, active cases, recoveriesand deaths. The values of these parameters give insight regarding the evolution of the pandemy and can revealdifferent policies and approaches in reporting the official data. For example one of them can indicate that in certaincountries a substantial amount of cases were reported only post­mortem. The variation across several countries ofanother parameter, which models the average convalescence time (the duration of the second stage of the infectiousstate), points to the fact that the recoveries are reported at different rates, in some cases with significant delays.Since it can be assumed that this is only a matter of reporting, we also perform additional simulations for thesecountries by taking the average convalescence time the value of Germany, which is the smallest within the wholerange. The conclusion is that under this assumption, the evolution of the active cases for example in Italy andSpain, is not significantly different to that in Germany, the comparison being based on the fact that these countriesshowed a similar number of cases within the considered period.

Global Transcriptome Analysis Identifies a Diagnostic Signature for Early Disseminated Lyme Disease and Its Resolution

ABSTRACT A bioinformatics approach was employed to identify transcriptome alterations in the peripheral blood mononuclear cells of well-characterized human subjects who were diagnosed with early disseminated Lyme disease (LD) based on stringent microbiological and clinical criteria. Transcriptomes were assessed at the time of presentation and also at approximately 1 month (early convalescence) and 6 months (late convalescence) after initiation of an appropriate antibiotic regimen. Comparative transcriptomics identified 335 transcripts, representing 233 unique genes, with significant alterations of at least 2-fold expression in acute- or convalescent-phase blood samples from LD subjects relative to healthy donors. Acute-phase blood samples from LD subjects had the largest number of differentially expressed transcripts (187 induced, 54 repressed). This transcriptional profile, which was dominated by interferon-regulated genes, was sustained during early convalescence. 6 months after antibiotic treatment the transcriptome of LD subjects was indistinguishable from that of healthy controls based on two separate methods of analysis. Return of the LD expression profile to levels found in control subjects was concordant with disease outcome; 82% of subjects with LD experienced at least one symptom at the baseline visit compared to 43% at the early convalescence time point and only a single patient (9%) at the 6-month convalescence time point. Using the random forest machine learning algorithm, we developed an efficient computational framework to identify sets of 20 classifier genes that discriminated LD from other bacterial and viral infections. These novel LD biomarkers not only differentiated subjects with acute disseminated LD from healthy controls with 96% accuracy but also distinguished between subjects with acute and resolved (late convalescent) disease with 97% accuracy. IMPORTANCE Lyme disease (LD), caused by Borrelia burgdorferi, is the most common tick-borne infectious disease in the United States. We examined gene expression patterns in the blood of individuals with early disseminated LD at the time of diagnosis (acute) and also at approximately 1 month and 6 months following antibiotic treatment. A distinct acute LD profile was observed that was sustained during early convalescence (1 month) but returned to control levels 6 months after treatment. Using a computer learning algorithm, we identified sets of 20 classifier genes that discriminate LD from other bacterial and viral infections. In addition, these novel LD biomarkers are highly accurate in distinguishing patients with acute LD from healthy subjects and in discriminating between individuals with active and resolved infection. This computational approach offers the potential for more accurate diagnosis of early disseminated Lyme disease. It may also allow improved monitoring of treatment efficacy and disease resolution.