Surface disinfection is important in the proper running of livestock farms. However, disinfection of farm equipment and facilities is difficult because they are made of different materials, besides having large surface areas and complex structures. 3-(trimethoxysilyl)-propyldimethyloctadecyl ammonium chloride (Si-QAC) is a quaternary ammonium salt-based disinfectant that attaches to various surfaces by forming covalent bonds and maintains its disinfecting capacity for a considerable time. Our aim was to evaluate the potential use of Si-QAC for disinfection of farm equipment and facilities.The short- and long-term antimicrobial and antiviral effects of Si-QAC were evaluated in both laboratory and farm settings using modified quantitative assessment method based on the standard operating procedures of the United States Environmental Protection Agency.Escherichia coli. Electron microscopy revealed that the mechanism underlying the disinfection activity of Si-QAC was associated with its ability to damage the outer membrane of the pathogen cells. In the field test, Si-QAC effectively reduced viral contamination of surfaces of equipment and space.Si-QAC was highly effective in controlling the growth of the Newcastle disease virus and avian pathogenic Our results suggest that Si-QAC has great potential as an effective chemical for disinfecting farm equipment and facilities. This disinfectant could retain its disinfection ability longer than other commercial disinfectants and contribute to better farm biosecurity. According to the report of the World Organization for Animal Health (OIE) World Animal Health Information System (WAHIS), more than 6,000 Highly Pathogenic Avian Influenza (HPAI) cases have occurred during 2016 through 2020, resulting in the disposal of more than 100 million poultries . Thus, dSelection of an appropriate disinfectant from the various commercially available ones depends upon the surface to be disinfected, target pathogen(s), and ambient environment. One challenge faced during surface disinfection is the surface structure, particularly equipment or large machines consist of multi-materials and subtle smaller parts that were too difficult to maintain clean status with a short routine cleaning. Moreover, it is difficult to maintain the disinfected status of a surface for a prolonged period because traditional disinfectants’ efficacy quickly decreases. To overcome this, coating substances with copper or silver, which have sterilizing properties, are often used ,5. MaterThe organosilicon quaternary ammonium salt 3-(trimethoxysilyl)-propyldimethyloctadecyl ammonium chloride (Si-QAC) has been recently developed as a surface disinfectant. Si-QAC has two distinctive parts—organosilane and a quaternary ammonium compound . The orgEscherichia coli (APEC) was provided by the Avian Disease Laboratory of Konkuk University .All tested surfaces  were fashioned into circular pieces  and autoclaved. The Newcastle disease virus (NDV) was obtained from KBNP Inc. , and the avian pathogenic 6 50% egg infectious dose [EID50]/mL; 100 μL) or APEC  using a micropipette by placing drops of the microbial culture suspensions onto the surface. To determine the minimum time required for antimicrobial and antiviral effects, disinfection efficacy was tested at 10, 20, and 30 min after contamination of uncoated/Si-QAC-coated surfaces. Antimicrobial and antiviral effects were measured by culturing the disinfectant-treated pathogens from the test materials in nutrient agar or in 10-day-old embryonated eggs . For long-term disinfection efficacy tests, the same procedure was followed, and the evaluation was performed every 10 days for 30 days.Disinfection efficacy was assessed using a modified quantitative assessment method based on the standard operating procedures of the United States Environmental Protection Agency . BrieflyThe morphology of disinfectant-treated pathogens was evaluated using field-emission scanning electron microscopy (FE-SEM). After 30 min of incubation on the test surfaces, the pathogens were collected and washed twice with phosphate-buffered saline (PBS). The samples were fixed in two stages—first in Karnovsky’s fixative for 6 h and then in 1% osmium tetroxide dissolved in 0.1 M PBS for 1.5 h. The fixed samples were dehydrated using 10% ascending gradual ethanol solu-tions starting from 60%, followed by treatment with isoamyl acetate. The samples were finally passed through a critical point dryer . Then, the samples were coated with platinum powder using an ion coater  and observed using a field-emission scanning electron microscope  at a voltage of 10 kV.Field tests at a hatchery  were conducted in cooperation with an integrated poultry company in Korea. The equipment and facility surfaces were made of either plastic or stainless steel. After cleaning the surfaces using hot water and detergent, Si-QAC was applied onto the equipment, including the egg trays and chick-hatching trays, and an egg incubation facility. Hundred microliters of NDV were then applied to the equipment and facility surfaces. After 30 min, the viruses were retrieved using sterilized cotton swabs and immersed in PBS; the residual viral titers in these suspensions were quantified by culturing the suspensions in 10-day-old embryonated eggs.All data were analyzed using the SPSS (version 23) software for Windows . Analysis of variance and Student’s t-test were used to determine the differences between groups. Statistical significance was set at p<0.05.We measured the minimum reaction time required for Si-QAC to exert its disinfection effects. Compared with the control groups, the counts of residual pathogens on the surfaces of most of the tested materials, except glass and stainless steel, in the experimental groups decreased from the 10-min time point. The viral and bacterial titers in all the tested surfaces from the experimental group were notably reduced at 30 min compared to those in all the tested surfaces of the control group .2 EID50/mL, which was significantly lower than in the control group. Further, the antimicrobial titer in the experimental group after 30 min was less than 10 CFUs/mL, which was significantly lower than in the control group (106 CFUs/mL).This disinfectant showed an antiviral effect starting from 10 min compared to the control group. After 30 min, the viral titer for all test surfaces in the experimental group was less than 10To evaluate the duration of the disinfection effect of Si-QAC on the test surfaces, its antiviral and antimicrobial effects were monitored over a month . In the Field-emission scanning electron microscopy analysis revealed that the outer membrane of the pathogen cells was damaged after treatment with Si-QAC . NDV parField tests were conducted at a fully operational hatchery. The test surfaces were treated with Si-QAC once, and viral titers from egg trays , chick-hatching trays , and an egg incubation facility  were evaluated. Disinfection effects were observed in the egg trays and chicken-hatching trays until the third assessment. The disinfection effect was maintained in the incubation facility until the second assessment, which was 36 days after artificial contamination with NDV .Salmonella spp, are harmful to animal health, farmers economic situation, by extension, poultry workers and consumers health. There is much evidence of these pathogens being transmitted to farms because of workers’ activities and tools used. Inappropriate defective disinfection could introduce pathogens to the farms and spread them to animals. Our results provide a basis for using Si-QAC as a surface-coating disinfectant in farm environments. A previous report showed that Si-QAC has chemical disinfec-tion effects through its quaternary ammonium component and that its organosilicon component causes physical damage to the cells of pathogens [In the poultry industry, pathogens such as avian influenza virus and athogens . Furtherathogens . The firathogens . The othathogens .Importantly, Si-QAC appears to be safe for use by humans; furthermore, it is a non-leaching material . This maThis study is the first report of Si-QAC application on farms. Although most results suggest that Si-QAC can be a potential disinfectant, additional data are required for maximizing the use of this disinfectant in different farm facilities. In livestock facilities, hot water high-pressure cleaning is routinely performed. This process may peel off the Si-QAC-coated layer and gradually decrease the disinfection efficacy. Information regarding factors such as the coating durability and optimal Si-QAC chain length for disinfecting surfaces to eliminate a diverse range of pathogens requires further elucidation.


Background and Objectives: Abnormal epileptic discharges in the brain can affect the central brain regions that regulate autonomic activity and produce cardiac symptoms, either at onset or during propagation of a seizure. These autonomic alterations are related to cardiorespiratory disturbances, such as sudden unexpected death in epilepsy. This study aims to investigate the differences in cardiac autonomic function between patients with temporal lobe epilepsy (TLE) and frontal lobe epilepsy (FLE) using ultra-short-term heart rate variability (HRV) analysis around seizures. Materials and Methods: We analyzed electrocardiogram (ECG) data recorded during 309 seizures in 58 patients with epilepsy. Twelve patients with FLE and 46 patients with TLE were included in this study. We extracted the HRV parameters from the ECG signal before, during and after the ictal interval with ultra-short-term HRV analysis. We statistically compared the HRV parameters using an independent t-test in each interval to compare the differences between groups, and repeated measures analysis of variance was used to test the group differences in longitudinal changes in the HRV parameters. We performed the Tukey–Kramer multiple comparisons procedure as the post hoc test. Results: Among the HRV parameters, the mean interval between heartbeats (RRi), normalized low-frequency band power (LF) and LF/HF ratio were statistically different between the interval and epilepsy types in the t-test. Repeated measures ANOVA showed that the mean RRi and RMSSD were significantly different by epilepsy type, and the normalized LF and LF/HF ratio significantly interacted with the epilepsy type and interval. Conclusions: During the pre-ictal interval, TLE patients showed an elevation in sympathetic activity, while the FLE patients showed an apparent increase and decrease in sympathetic activity when entering and ending the ictal period, respectively. The TLE patients showed a maintained elevation of sympathetic and vagal activity in the pos-ictal interval. These differences in autonomic cardiac characteristics between FLE and TLE might be relevant to the ictal symptoms which eventually result in SUDEP. Epilepsy is the second-most functionally distressing neurological disease worldwide and affects 6.38 out of 1000 people . The mosAlthough the mechanism underlying autonomic dysfunction in epilepsy patients is not fully elucidated, alteration in cardiac ion channel expression and the detrimental effects of cortical epileptic discharge on centrally mediated cardiac outputs are the suggested pathophysiology . AbnormaHeart rate variability (HRV) analysis is considered one of the most accurate indicators of sympathovagal balance in the autonomic nervous system (ANS) . RecentlConventional HRV analysis requires a long-term heart rate sequence . Long-term HRV is useful to assess the homeostatic mechanisms related to circadian rhythms, the core body temperature, sleep cycle and general metabolism. Therefore, long-term HRV has been considered the gold standard for clinical HRV assessment . For exaRecently, several investigations have shown the possibility for HRV analysis with a shorter recording interval. This approach is called ultra-short-term HRV analysis, which refers to HRV analysis with less than five-minute intervals. Several researchers have investigated the reliability of this method. For example, Salahuddin et al.  showed tWe hypothesized that ultra-short-term HRV analysis could be useful to assess the autonomic alterations around the ictal interval, which are related to the differences in the post-ictal conditions between epilepsy types. The objective of this study was to assess the differences in autonomic patterns within each interval  between patients with FLE and patients with TLE using ultra-short-term HRV analyses.We retrospectively reviewed the medical records of the Samsung Medical Center Epilepsy Monitoring Unit between 2014 and 2019. Subjects were identified from a database with the following criteria: (1) definite TLE or FLE with electrophysiologic, semiologic and imaging findings, (2) the presence of ictal tachycardia, defined by HR > 100 bpm or a >20% increase in the baseline HR, (3) identifiable QRS complexes on an ictal ECG, (4) partial seizure without secondary generalization and (5) more than 180-s intervals between ictal events. Patients who had probable psychological paroxysmal events were excluded from this study.This study included 58 patients  between 8 and 71 years of age (mean age: 33.1 ± 15.7). Among them, 12 patients had frontal lobe epilepsy and 46 patients had temporal lobe epilepsy. The group of patients with frontal lobe epilepsy consisted of 3 females and 9 males with an age range of 8–70 years (mean age: 18.3 ± 7.8 years). The mean number of seizures for each patient with frontal lobe epilepsy was 5.8 (±3.4), and the mean ictal duration was 79.4 (±59.9) s. The group of patients with temporal lobe epilepsy consisted of 19 females and 27 males between 18 and 71 years of age (mean age: 36.5 ± 14.4 years). The mean number of seizures for each patient with temporal lobe epilepsy was 5.2 (±3.2), and the mean ictal duration was 98.8 (±50.4) s.All patients underwent presurgical noninvasive evaluations, which included a video-EEG recording of habitual seizures, 3.0-Tesla brain magnetic resonance images (MRIs), 18F-FDG fluorodeoxyglucose (918F-FDG) positron emission tomography (PET) scans, subtracted 99m-Tc ECD ictal single-photon emission computed tomography scans, a complete neuropsychological examination and intracarotid amobarbital tests. For the presurgical evaluation, the patients were hospitalized in the epilepsy monitoring units to monitor their semiology and biosignals, such as by EEG and ECG. After these studies were reviewed in multidisciplinary intensive epilepsy conferences, the type of epilepsy and therapeutic strategies were identified.This study was approved by the Institutional Review Board of Samsung Medical Center  and conducted under the principles of the Declaration of Helsinki. The requirement for individual informed consent for this study was waived by the Institutional Review Board, as it was a retrospective study using electronic medical records.According to the inclusion criteria, we retrospectively collected the electrocardiogram recordings of 309 seizure events (70 for FLE and 239 for TLE) from 58 patients (12 for FLE and 46 for TLE). The ECG recordings were conducted in the epilepsy monitoring unit. During hospitalization in the epilepsy monitoring unit, patients stopped taking antiepileptic drugs to inspect the semiology during seizures. The electroclinical data were reviewed separately by two epileptologists, who determined and marked the onsets and ends of seizures based on a scalp EEG to define the ictal interval. The EEG signals which could provide the most exact timing of a seizure were used to precisely define the onset and offset of each seizure. Along with the annotation from the EEG signals, we analyzed the ECG signals from three intervals. The first interval was the pre-ictal period, which was defined as the three minutes before seizure onset. The second interval was the post-ictal period, defined as the three minutes after seizure offset. Lastly, the ictal interval was the period during the seizure. The demographic and clinical data of the subjects are described in ECG data were collected using the NicoletOne LTM system  with a sampling rate of 512 Hz. The ECGs were recorded with two electrodes located at the shoulders to record the lead 1 channel. After the ECG signals were acquired, we filtered them to remove noise due to subject movements, breathing and muscle electrical activity. We applied a third-order Butterworth 0.5 Hz high-pass filter to remove baseline wander and a 60-Hz notch filter to remove powerline interference . A movinWith the filtered ECG signal, we extracted the R peak and the RR interval with the wavelet transform-based QRS complex detector method . EctopicThe HRV parameters were extracted from the time and frequency domains . For timThe power of the heart rate spectrum was divided into two components: low frequency (0.04–0.15 Hz) and high frequency (0.15–0.4 Hz). The power of each band was normalized by its ratio with the total power of the heart rate spectrum, except for the very low frequency band (less than 0.04 Hz). It is generally agreed that the LF band is influenced by both sympathetic and vagal activity, and the HF band represents only vagal activity ,54,55. Tp-value < 0.05 was considered statistically significant. First, we compared the HRV parameters in each interval to determine the differences between groups using the independent t-test for each ictal record. Then, repeated measures analysis of variance (ANOVA) was used to assay the group differences in the longitudinal HRV changes for the three intervals  as the within-subjects factor and the group (FLE and TLE) as the between-subjects factor. As a post hoc test, we performed the Tukey–Kramer multiple comparisons test (α = 0.05) [Statistical analyses were performed with Stata, version 15 . A  = 0.05) .p = 0.012 for pre-ictal and p < 0.001 for ictal and post-ictal). For the frequency domain HRV parameters, the normalized LF was statistically significantly different in the pre-ictal interval (p = 0.023) and ictal interval (p < 0.001) compared with the other groups. The LF/HF ratio was significantly distinct in the ictal interval (p = 0.010).The results of the independent t-test for each interval are described in p < 0.001 for mean RRi and p = 0.011 for RMSSD). However, those two parameters did not significantly interact with the interval. On the other hand, the interaction between the epilepsy type and the interval was statistically significant for the normalized LF (p < 0.001) and LF/HF (p = 0.013).In Multiple post hoc comparisons showed that the FLE patients had significantly increased normalized LF for the ictal interval  compared with that of the pre-ictal interval  . In addiThe normalized LF was reduced immediately in the post-ictal interval  and showed a significant decrease from the ictal interval and no significant difference with that of the pre-ictal interval. On the other hand, TLE showed a modest decrease in the LF band from the pre-ictal interval to the post-ictal interval, but there were no significant differences in the normalized LF between intervals . However, the LF/HF ratio showed interesting results. The FLE patients showed a significant increase in the LF/HF ratio for the ictal interval  compared with that of the pre-ictal interval  and decreased immediately in the post-ictal interval . On the contrary, the TLE patients showed a significant increase in the ictal interval  compared with the pre-ictal interval , but the increase was prolonged in the post-ictal interval  and showed no significant difference between the ictal and post-ictal intervals .This study aimed to investigate the cardiac autonomic differences between temporal lobe epilepsy and frontal lobe epilepsy by analyzing heart rate variability during the pre-ictal, ictal, and post-ictal intervals.Most of the HRV parameters in the time and frequency domain changed significantly in the ictal interval . In the Among the frequency domain parameters, the observed combined increment of the LF/HF ratio supports the presence of higher sympathetic activity during the ictal interval. Many previous studies have reported those sympathetic dominance features in the interictal condition ,39,40,60In a comparison of HRV parameters between frontal lobe epilepsy and temporal lobe epilepsy patients, we found that the TLE patients had a lower mean RRi during the pre-ictal, ictal and post-ictal periods compared with the FLE patients. Since the mean RRi is inversely correlated with the degree of tachycardia, the observed increases in the mean RRi in TLE patients indicate that ictal tachycardia was more prominent in this group, which is consistent with our previous report . In detaBoth types of epilepsy are associated with an increase in HR during the ictal interval, which is related to ictal tachycardia, an autonomic manifestation of seizures ,62. PrevIn this study, a pattern of HRV in the TLE patients was characterized by pre-ictal elevation of the sympathovagal tone and subsequent increase and maintenance in sympathetic activity during the ictal and post-ictal periods. On the contrary, the FLE patients were marked by an abrupt rise and fall of the sympathetic tone when entering and leaving the ictal period. An enhanced sympathovagal state in the pre-ictal TLE patients can be explained by anatomic resemblance or proximity of epileptogenic focus in the TLE to the adjacent limbic system, such as the amygdala, hippocampus or insula, which are known parts of the central autonomic network .FLE patients, on the other hand, are characterized by a shorter seizure duration and more frequent secondary generalization than TLE patients, and these characteristics can be related to a more abrupt and stronger rise in the sympathetic tone during the ictal period in FLE patients. Since the thalamus is a subcortical structure known to be involved in ictal cardiac changes as well as in seizure generalization , it can In the temporal lobe, the amygdala is highly and widely connected with the autonomic structures of the hypothalamus and brainstem that can induce complex autonomic disturbances. Enhanced vagal activity in TLE patients during the ictal and post-ictal periods can be a compensatory mechanism against the prolonged elevation in the sympathetic tone observed in pre-ictal and post-ictal intervals. Meanwhile, the finding that an elevated sympathetic tone in the TLE patients remained high in the post-ictal period can be related to the phenomenon of prolonged post-ictal confusion in this group , becauseThere are some limitations to this study. Although we ascertained the differences in HRV between TLE and FLE patients, the sample size was relatively small. Even though ictal tachycardia is the most common symptom among the cardiorespiratory disturbances in patients with epilepsy, further studies with other cardiorespiratory disturbances like ictal bradycardia and ictal asystole are needed for the understanding and prevention of SUDEP. Additionally, we focused on HRV patterns around the ictal interval with ultra-short-term HRV analysis. However, the HRV was also affected by the circadian rhythm and antiepileptic drugs. Therefore, a future study that considers the effects of a circadian rhythm and antiepileptic drugs is recommended. The HRV is also under the effect of circadian variation in its nature. The alternation of HRV around the ictal period is directly due to seizures, but there may be an underlying effect of the circadian variation. Likewise, even though the included patients stopped taking antiepileptic drugs during their hospitalization, there could be long-lasting effects of previously taken antiepileptic drugs on ANS, which may cause alternations in HRV. Therefore, to clarify those underlying effects on HRV during seizures, further studies are recommended to be performed with cohorts monitored for a long term.In this study, we investigated the differences between TLE and FLE patients with ultra-short-term HRV analysis. We showed that the patients with TLE and FLE had different HRV profiles in the pre-ictal, ictal and post-ictal intervals. The TLE patients exhibited elevated sympathetic or vagal activity during the pre-ictal condition, while the FLE patients showed a marked increment and decrement in sympathetic tone when entering and leaving the ictal period. The TLE patients showed sustained post-ictal elevation in sympathetic and vagal activity. These results could be useful in two ways. First, the acute alteration of cardiac functions during seizures could give information on lateralization of the seizure, since the central autonomic control centers could be hemispherically localized. Secondly, these findings could be used to understand the underlying pathophysiological mechanisms of SUDEP. The exact mechanism of SUDEP is still unknown and multifactorial, but cardiovascular dysfunction could be one of the triggers. Therefore, the ultra-short-term HRV analysis, which is useful to monitor the acute sympathetic or vagal activity around the ictal, could predict the risk of fatal cardiovascular malfunctions in the future.


The COVID-19 poses a disproportionate threat to nursing home residents. Although recent studies suggested the effectiveness of state social distancing measures in the United States on curbing COVID-19 morbidity and mortality among the general population, there is a lack of evidence as to how these state orders may have affected nursing home patients or what potential negative health consequences they may have had. In this longitudinal study, we evaluated changes in state strength of social distancing restrictions from June to August of 2020, and their associations with the weekly numbers of new COVID-19 cases, new COVID-19 deaths, and new non-COVID-19 deaths in nursing homes of the US. We found that stronger state social distancing measures were associated with improved COVID-19 outcomes (case and death rates), reduced across-facility disparities in COVID-19 outcomes, and somewhat increased non-COVID-19 death rate, although the estimates for non-COVID-19 deaths were sensitive to alternative model specifications. COVID-19 is known to have been present in almost all of the more than 15,500 nursing homes in the United States. By December 23, 2020, over 905,000 coronavirus cases and 113,000 associated deaths were reported in LTC facilities, representing 6% and 39% of total COVID-19 cases and deaths, respectively6.The coronavirus disease 2019 (COVID-19) poses a disproportionate threat to older adults especially those residing in long-term care (LTC) facilities such as nursing homes or assisted living facilities8 largely mirror the disparities found in the general population12. In a national study of nursing homes, Li and colleagues revealed that COVID-19 cases and deaths per facility were 2 to 4 times higher in nursing homes with highest proportions of racial/ethnic minority residents than in nursing homes with low proportions7. These disparities are largely a result of system-level inequalities and segregation of care in that older residents of color are disproportionately cared for in facilities that are located in marginalized communities, have inadequate resources and limited abilities to respond to outbreaks of emerging infections, and provide care of poorer quality13.Recent studies suggest that racial/ethnic disparities in COVID-19 morbidity and mortality among LTC residents15. Delay or avoidance of necessary medical care because of concerns about COVID-19 and social distancing restrictions increases morbidity and mortality risk associated with the health conditions of older adults, and may contribute to reported excess deaths17. Moreover, disruptions of in-person social activities during the pandemic have led to increase in social isolation and loneliness among older adults, another serious public health issue that is associated with psychological suffering, unmet personal and health care needs, and deaths21.During the pandemic, deaths from non-COVID-19 causes, such as Alzheimer disease, diabetes, and heart disease, also increased markedly, mostly among older adults22. Recent studies suggested the effectiveness of these state social distancing measures on curbing COVID-19 cases, hospitalizations, and associated deaths among the general population26. However, there is a lack of evidence as to how these state orders may have affected the most vulnerable groups, such as LTC patients, or what potential negative health consequences they may have had, such as excess mortality due to reasons other than COVID-19 infection.Starting from early March of 2020, most states in the US implemented non-pharmaceutical public health interventions to contain coronavirus transmission. These policies took a variety of forms—such as statewide shelter-in-place orders, closure of non-essential businesses, and bans on large and small-group gatherings—and were implemented at different times and with different levels of enforcement across statesIn this longitudinal study, we evaluated changes in state strength of social distancing restrictions from June to August of 2020, and their associations with the weekly numbers of new COVID-19 cases, new COVID-19 deaths, and new non-COVID-19 deaths in nursing homes nationally. We hypothesized that stronger state COVID-19 restrictions led to reduced COVID-19 case and death rates, reduced disparities in COVID-19 case and death rates between nursing homes with high and low proportions of racial/ethnic minorities, but increased non-COVID-19 mortality rate among nursing home residents.Table In bivariate analyses Table  and for Multivariable longitudinal analyses Table  largely 8. Table Existing evidence suggests across-facility disparities in COVID-19 case and death ratesFor the week of August 17–23, non-COVID-19 death rates were higher in nursing homes in states with high compared to those in states with low COVID-19 restrictions , has immediately refocused their quality of care inspections in nursing homes on compliance with infection control policies and practices27. The CMS and the CDC also released guidelines and rules to combat the surge of COVID-19 infections and deaths in LTC facilities such as restrictions on nursing home visitors including non-essential health care personnel28; cancellation of all group activities and communal dining in nursing homes; implementation of active screening of residents and health care personnel for fever and respiratory symptoms29; and a mandate to separate staff members dedicated to the care of residents with COVID-19 from those caring for residents without (or whose COVID-19 status is unknown)30.Since early March of 2020 and in the wake of the novel coronavirus outbreak at a skilled nursing facility in Washington state24. The details of these social distancing restrictions, and their implementation or enforcement, vary over states and over time as the pandemic progresses; for example, many states partially reopened non-essential businesses in early summer of 2020 but closed these businesses again due to surges of COVID-19 in the second wave of the pandemic. Although recent studies examined variations of these state social distancing measures and demonstrated their effectiveness on reducing COVID-19 related morbidity and mortality26, the state COVID-19 policies examined in these studies were generally limited to the early stage of the pandemic (i.e. March to May of 2020). It thus remains uncertain if the effect of strong state COVID-19 restrictions has been maintained as the pandemic continues to evolve, and if prolonged restrictions may lead to serious negative health consequences such as excess mortality.In addition to these federal guidelines specifically designed to curb transmission of the virus in LTC facilities, most states in the US implemented social distancing measures to contain the growth rate of infections among the general population following the experiences of countries attacked earlier by the pandemicThese expected state policy effects, positive or negative, would likely manifest first and strongest among nursing home residents who represent the most vulnerable group and who are hit the hardest throughout different stages of the pandemic. However, thus far, there has been a lack of studies to determine the effect of early state  responses to the pandemic on COVID-19 outcomes among nursing home residents, largely due to the absence of nursing home COVID-19 data nationally during early period of the pandemic.31, and a later interim final rule of the CMS on May 1 marked the formal start of this national data submission process. These data were first publicly published on June 4, containing facility-level counts of COVID-19 cases and deaths since late May; they continue to be updated weekly by the CMS. Taking advantage of this new data reporting system and state variations in the restrictiveness of their social distancing policies, this study found stronger state COVID-19 restrictions were associated with reduced nursing home COVID-19 infection and fatality rates, results that one would expect to see. Of note, this study was not able to determine the effect of above-mentioned federal responses to the COVID-19 outbreaks in LTC facilities. These federal guidelines and rules likely helped avoid a large number of COVID-19 cases and deaths in all nursing homes in the US, but their exact effects are difficult, if not impossible, to determine due to the lack of a comparison group . The variation in state COVID-19 restrictions provide a natural experiment for investigating the effect of state policies. In addition, several features of this study, such as its longitudinal design and the control of the secular trend and the cumulative number of COVID-19 cases (or deaths) right before state COVID-19 policies were ranked in multivariable analyses, allowed us to estimate the independent effect of state COVID-19 policies that is above and beyond the effect of federal guidelines and rules designed to curb coronavirus transmissions in all nursing homes.In an effort to fill this information gap, the CMS released guidance on April 19 that required nursing homes to report COVID-19 cases and deaths directly to the CDC12. A most recent study of the Kaiser Family Foundation12 has analyzed electronic health records of 50 million patients and estimated that although there is little difference in the testing rates by race and ethnicity, Black, Hispanic, and Asian patients are about two-to-three times as likely to test positive as Whites. Moreover, hospitalization and death rates are at least twice as high among people of color as among White patients, and these disparities persist after accounting for differences in sociodemographic characteristics and underlying health conditions.The stronger effect of state COVID-19 policies on improved COVID-19 outcomes for nursing homes that disproportionately care for more racial/ethnic minority residents suggests an additional disparity reduction effect of these state policies. Since the beginning of the global COVID-19 pandemic in March, compelling evidence has indicated that racial/ethnic minority persons are at higher risk of COVID-19 infections, hospitalizations, and deaths, compared to non-Hispanic whites33, largely due to system-level inequalities and segregation of care. These disparities persisted in the past several decades despite the evidence of overall improved quality of care and outcomes for all residents and nursing homes over time. The across-facility disparities in COVID-19 morbidity and mortality reported by recent studies8 are just another manifestation of the structural inequalities in nursing home care that have existed for decades. For example, nursing homes serving disproportionately non-white residents tend to be faced with serious resource constrains and operate within local healthcare systems that are also resource strained and thus easily overwhelmed by the pandemic.Racial/ethnic disparities in outcomes of nursing home care have existed long before the COVID-19 pandemic34. Under the Provider Relief Fund authorized by the CARES Act, the US federal government has allocated $10 billion to nursing homes thus far, which helped all facilities in the nation to address shortages in personal protective equipment (PPE) and staff and to improve testing capacities35. Nevertheless, these federal funds are “color blind” and do not explicitly target systemic inequalities in nursing home care under the COVID-19 pandemic36. It is unknown if and how this federal aid may help to address the across-facility disparities in COVID-19 outcomes.The Coronavirus Aid, Relief, and Economic Security (CARES) Act enacted on March 27, 2020 in the United States intended to blunt the impact of economic downturn due to pandemic through financial aid to businesses, individuals, and healthcare institutionsOur findings of the reduced disparities in nursing home COVID-19 outcomes as a result of stronger state social distancing stipulations suggest the possibility that broadly targeted policy and public health interventions are able to both mitigate coronavirus transmissions and redress outcome disparities due to enduring system-level inequalities. Although the exact mechanism through which disparities are reduced is not clear, it appears that certain level of stringency in social distancing restrictions are particularly necessary for the subgroups of minority-serving and resource poor nursing homes to effectively reduce virus transmissions among their residents and staff. It is also possible that in states with low strength of COVID-19 restrictions, the weekly COVID-19 case and mortality rates for nursing homes caring for low proportions of racial/ethnic minority residents were already very low . Nevertheless, these estimates do not rule out the possibility of excess mortality due to restrictive state social distancing rules and future research is needed to determine if strong social distancing restrictions over longer period of time may lead to stronger and more consistent negative outcomes including non-COVID-19 mortality.Concerns about the downside effect of prolonged social distancing restrictions have been expressed, although empirical evidence is limited on how these restrictions affect the physical and mental health outcomes of individualsThis study has several limitations. First, we were only able to track state social-distancing measures and nursing home COVID-19 and non-COVID-19 outcomes starting in June due to the lack of national data in early period of the pandemic. Second, although we demonstrated that in aggregate, stronger state social distancing measures had both positive and negative health consequences on nursing home residents, we cannot determine the effect of individual state policies or orders. Finally, our ability to adjust for nursing home, county, and state covariates may be somewhat limited in multivariable analyses. Therefore, the estimated associations may be partially mediated by unmeasured factors that affect COVID-19 outcomes and non-COVID-19 deaths among nursing home residents.In conclusion, we found that stronger state social distancing measures were associated with improved COVID-19 outcomes (case and death rates), and reduced across-facility disparities in COVID-19 outcomes. Future work is needed to determine if prolonged state social distancing rules may lead to unintended health consequences, including non-COVID-19 mortality, for nursing home residents.https://data.cms.gov/stories/s/COVID-19-Nursing-Home-Data/bkwz-xpvg). This file contains weekly counts of incident COVID-19 cases and deaths among nursing home residents and staff separately, starting from May 25, as submitted by individual nursing homes through CDC’s National Healthcare Safety Network (NHSN) system (COVID-19 LTC Facility Module). These counts were also reported for the week ending on May 24; however, numbers reported for that week may include both new cases (or deaths) in the week and cases (or deaths) identified before that week, which makes the data inappropriate for analyses due to unknown starting dates of reporting for individual nursing homes7. For subsequent weeks, nursing homes reported only new cases and deaths identified in each reporting week. CMS and CDC performed data quality checks to ensure the accuracy of the reported numbers.The first source of data is the Nursing Home COVID-19 Public File published and updated by the Centers for Medicare and Medicaid Services . Each metric was graded, by an expert panel, using a 100-point scale, with higher scores representing fewer restrictions; the panel then determined the weighted average across all metrics for each state and used the overall scores to rank-order all 50 states and the District of Columbia. The overall weighted scores and rank orderings were published and updated every 2 to 3 weeks  starting from May. We downloaded data of state rankings on COVID-19 restrictions in 5 consecutive updates on the following dates: June 9, June 23, July 7, July 21, and August 11.We also obtained data on the stringency of state social distancing measures from 37 and the median time from illness to death is 18.5 days38.We linked these state data to nursing home COVID-19 reports assuming a 1-week lag of state policy effect on new nursing home cases , and a 3-week lag on new deaths . We assumed lagged effects of change in state COVID-19 restrictions because it is estimated that the median incubation period of COVID-19 is 5 days39; (2) the LTCFocus file created by the Brown University for additional nursing home characteristics; (3) the Area Healthcare Resource File for key county covariates; and (4) the numbers of lab-confirmed COVID-19 cases and deaths for all counties and states obtained from the national database published by the New York Times (https://github.com/nytimes/covid-19-data). These numbers have been compiled and updated in real time by the Times based on reports from state and local public health agencies.We then linked this longitudinal database to several other data files including: (1) the CMS Nursing Home Compare (NHC) data files (updated in August of 2020) to obtain important covariates of nursing home organizational, staffing, and quality of care measuresThe 4 outcomes of interest included nursing home weekly numbers of: (1) new, laboratory-confirmed COVID-19 cases among residents; (2) new, lab-confirmed COVID-19 cases among staff; (3) new COVID-19 related deaths among residents; and (4) new non-COVID-19 related deaths among residents, for each of the 5 reporting weeks described above.8, we categorized nursing homes into quartiles to capture possible nonlinear associations between racial composition and COVID-19 outcomes: nursing homes with low proportions of racial/ethnic minority residents , medium proportions , medium–high proportions (11.11–29.79%), and high proportions .The key independent variable was state rankings on COVID-19 restrictions, which, for ease of presentation, was dichotomized as 1 for states with high stringency (rankings of 26th–51st) and 0 for states with low stringency (rankings of 1st–25th). In sensitivity analyses state rankings were included as a continuous variable and, alternatively, were re-categorized as tertile groups in regression analyses. Another key independent variable for the analyses on across-facility disparities in COVID-19 cases and deaths was percentage of racial/ethnic minority residents  in the nursing home (obtained from the LTCFocus file) which was originally defined using the race and ethnicity information from the Minimum Data Set and Medicare enrollment databases. Following recent studies of across-facility disparities in COVID-19 outcomes8 were included in regression analyses: number of beds, average daily resident census, ownership status , chain affiliation, hospital affiliation, percentage of Medicare residents, percentage of Medicaid residents, a facility-level case mix index, average staffing levels (hours per resident day) for registered nurse (RN) and for all nursing staff  in 2019, and five-star ratings for overall quality of care. RN and other nurse staffing levels were calculated based on daily resident census and CMS’ Payroll-Based Journal system through which nursing homes electronically submit the number of hours that agency and contract staff are paid to work each day40. The five-star ratings aggregate ratings of nursing home quality measures on three domains—deficiency citations assigned during on-site inspections, care processes and outcomes of residents, and nurse staffing to resident ratios—into a rating system of one to five stars, with more stars indicating better quality41. Additional nursing home covariates included the cumulative numbers of COVID-19 cases and COVID-19 and non-COVID-19 deaths among residents before state COVID-19 policies were evaluated, which were calculated for the period from May 25 to 2 days before the strength of the state social distancing measures was ranked.The following nursing home covariates that were found important to COVID-19 infections or deathsCounty-level covariates included cumulative number of COVID-19 confirmed cases, cumulative number of COVID-19 deaths, median household income, and county population size. State-level covariates included cumulative number of COVID-19 confirmed cases per 1000 population, cumulative number of COVID-19 deaths per 1000 population, percentage of older population (≥ 65 years), and percentage of non-white population. All county and state cumulative counts were calculated for the period from May 25 to 2 days before the strength of the state social distancing measures was ranked.We compared differences in nursing home, county, and state characteristics by strength of state COVID-19 restrictions (high vs low) and by nursing home quartile groups of racial/ethnic compositions. T-tests or analyses of variance for continuous variables and chi-square tests for categorical variables were used for statistical inference.42. The first part of the models was a generalized linear model with a logit link function and an assumed binomial distribution, which estimated the likelihood of a nursing home to have at least one new confirmed case (or death) reported in the week. The second part of the two-part models was a count model that assumed negative binomial distribution to account for the over-dispersion of event occurrence and estimated the number of nursing home new cases (or deaths) conditional on at least one new case (or death) confirmed in the reporting week.In multivariable analyses, we fitted separate longitudinal two-part models, with unit of analysis being the nursing home week, to account for the fact that a large number of nursing homes had zero cases (or deaths) in each study week43. After model estimation, we obtained the predicted event counts and plotted each predicted count against different levels of state policy stringency and nursing home racial composition groups. The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Research Subjects Review Board of University of Rochester.Both parts of the models had an indicator for high vs. low strength of state COVID-19 restrictions as the independent variable and controlled for the same nursing home, county, and state covariates, as well as a set of indicators for study weeks (secular trend). In estimating the associations of state policy with across-facility disparities in COVID-19 related outcomes, the two-part models further included interaction between the state policy indicator and indicators for medium, medium–high, and high concentrations of racial/ethnic minority residents in nursing homes (the low concentration group serving as the reference group). All coefficient estimates were based on Huber–White robust standard errors to correct for the correlation of nursing home outcome when it was repeatedly observed over 5 weeksSupplementary Tables.


This study intends to establish a combined prediction model that integrates the clinical symptoms,the lung lesion volume, and the radiomics features of patients with COVID-19, resulting in a new model to predict the severity of COVID-19.The clinical data of 386 patients with COVID-19 at several hospitals, as well as images of certain patients during their hospitalization, were collected retrospectively to create a database of patients with COVID-19 pneumonia. The contour of lungs and lesion locations may be retrieved from CT scans using a CT-image-based quantitative discrimination and trend analysis method for COVID-19 and the Mask R-CNN deep neural network model to create 3D data of lung lesions. The quantitative COVID-19 factors were then determined, on which the diagnosis of the development of the patients' symptoms could be established. Then, using an artificial neural network, a prediction model of the severity of COVID-19 was constructed by combining characteristic imaging features on CT slices with clinical factors. ANN neural network was used for training, and tenfold cross-validation was used to verify the prediction model. The diagnostic performance of this model is verified by the receiver operating characteristic (ROC) curve.CT radiomics features extraction and analysis based on a deep neural network can detect COVID-19 patients with an 86% sensitivity and an 85% specificity. According to the ROC curve, the constructed severity prediction model indicates that the AUC of patients with severe COVID-19 is 0.761, with sensitivity and specificity of 79.1% and 73.1%, respectively.The combined prediction model for severe COVID-19 pneumonia, which is based on deep learning and integrates clinical aspects, pulmonary lesion volume, and radiomics features of patients, has a remarkable differential ability for predicting the course of disease in COVID-19 patients. This may assist in the early prevention of severe COVID-19 symptoms. Pneumonia is a highly contagious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection that emerged in December 2019 , 4. COVICurrently, the sarS-COV-2 reverse transcription polymerase chain reaction (RT-PCR) is the preferred method for the detection of COVID-19 . HoweverAI technology is a diagnostic assistance technology that has progressed rapidly in recent years, with impressive achievement in many medical domains –16. As aCritically ill patients with COVID-19 pneumonia have a significant fatality rate. 1.6% of active cases are in a severe or critical condition , and theAccording to the standards of COVID-19 Diagnosis and Treatment Protocol  , this reThe included patients met the following diagnostic criteria: high-throughput sequencing or RT-PCR of nasopharyngeal swab specimens were positive. According to the standards of the COVID-19 Diagnosis and Treatment Protocol , this research divides COVID-19 pneumonia into mild, moderate, and severe types. Since there are no pneumonia indications in the images of patients with mild symptoms, the study classified mild pneumonia as moderate pneumonia as well, in order to distinguish clinically diagnosed severe COVID-19 pneumonia from the other two types.In order to train and evaluate our semantic segmentation framework, we manually segmented 100 CT slices manifesting COVID-19 features from 10 patients. Annotation was done through polygons. The segmentation labels were used to distinguish the relevant pathological features of COVID-19 pneumonia from other common pneumonia. The annotation included lung fields and five commonly seen lesion categories, including Compliance of Lung (CL), ground glass shadow, pulmonary fibrosis, interstitial thickening, and pleural effusion. Three senior radiologists with 15 to 25 years of expertise annotated and assessed the segmentation.In order to analyze the CT images of patients, all images were selected for quality control by deleting any scans that were low-quality or unreadable. All images were subjected to a hierarchical grading system that included two levels of qualified grading professionals with good professional expertise who could verify and correct the image labels. Each image that was imputed into the database began with a label that matched to the patient's diagnostic results. This was an initial quality check performed by radiologists with 5 to 15 years of clinical practice experience who acted as first-level graders to exclude images with serious artifacts or with significantly reduced image resolution. Then they looked at the CT images to see whether there were any lung lesions.This research provides a CT-image-based COVID-19 pneumonia quantitative discrimination and trend analysis algorithm based on, through the Mask R-CNN deep neural network model , which eThe ROC and AUC were used to evaluate model performance. Sensitivity, specificity, and accuracy were determined by the selected operating point. The operating point between the low false-negative diagnosis rate (sensitivity) and the low positive diagnosis rate (1 − specificity) was set at different thresholds. The Pearson and Spearman correlation test of the Holm-Bonferroni Method was used for statistical analysis. The training, verification, and testing procedures of the deep learning model were carried out by using Pytorch (v.1.2.0). We used the Python scikit-learn library for data analysis  and usedOf 386 confirmed patients with COVID-19 included in this research, 205 had CT image specimens (Table Based on the characteristics of Mask R-CNN  transferThe polygonal contours on the CT cross-section of the lungs were the focuses of infection predicted by the model Fig. . Based oFollowing that, we employed artificial neural networks to create a prediction model for the severity of COVID-19 by combining distinctive imaging features on CT and clinical parameters. Among the 205 COVID-19 patients, 140 patients with both CT imaging and clinical data were selected, with 70 critically ill and 70 mildly ill. 11 imaging features were extracted from CT samples using the deep neural network and combined with 17 clinical measurement indicators during the hospitalization of the patient. The SelectKBest method was used to select the best 15 feature combinations from 28 features (Table The research takes AI imaging technology as the core; CT imaging sample data of patients with COVID-19 as a motivator; servers, databases, and human–computer interaction interfaces as carriers to build an AI imaging platform with practical value. Relevant medical workers can log into the platform Fig.  and use The platform can display lesion images, parameters, variation tendency of the disease, etc. Fig. . The lesCOVID-19 is an acute contagious disease with a high transmission rate and spreading rapidity, which has caused a global pandemic . Chest CIn recent years, an artificial intelligence imaging diagnosis system that can perform quantitative analysis and differential diagnosis of lung inflammation has become a research hotspot . AI techThis research builds an early warning model for severe COVID-19, which has a certain innovative contribution. Firstly, it overcomes the bottleneck of predictingwhether patients will become critically ill in the diagnosis and treatment of COVID-19 , and buiHowever, there are certain limitations to this study. The small sample size is one of the most significant limitations. Although the results of utilizing AI models to diagnose and predict whether COVID-19 patients will become severe are encouraging, more data is needed to validate the model's universality. Moreover, the model's training and verification are limited to a small number of domestic populations, and we hope that international populations can be employed to further validate and increase the model's universality. It is important to note that the AI image recognition platform built by this research is extensible.The AI image recognition platform can not only seamlessly include other lung diseases into the platform, but also other diseases based on image recognition, such as the identification of restricted airways (Fig. In conclusion, based on deep learning, the combined prediction model for severe COVID-19 pneumonia has a strong differential ability for predicting the course of disease in COVID-19 patients by combining clinical features, pulmonary lesion volume, and radiomics features. This may assist in the early prevention of severe COVID-19 symptoms.


Despite the unquestionable success achieved by rituximab-based regimens in the management of diffuse large B-cell lymphoma (DLBCL), the high incidence of relapsed/refractory disease still remains a challenge. The widespread clinical use of chemo-immunotherapy demonstrated that it invariably leads to the induction of resistance; however, the molecular mechanisms underlying this phenomenon remain unclear. Rituximab-mediated therapeutic effect primarily relies on complement-dependent cytotoxicity and antibody-dependent cell cytotoxicity, and their outcome is often compromised following the development of resistance. Factors involved include inherent genetic characteristics and rituximab-induced changes in effectors cells, the role of ligand/receptor interactions between target and effector cells, and the tumor microenvironment. This review focuses on summarizing the emerging advances in the understanding of the molecular basis responsible for the resistance induced by various forms of immunotherapy used in DLBCL. We outline available models of resistance and delineate solutions that may improve the efficacy of standard therapeutic protocols, which might be essential for the rational design of novel therapeutic regimens. Non-Hodgkin lymphoma (NHL) is the most frequent hematological neoplasm in the world  with morThe most common first-line treatment for DLBCL is chemoimmunotherapy containing rituximab, the so-called R-CHOP regimen , which fails in 30–40% of patients. Although relapsed/refractory (r/r) patients receive second-line treatment or may undergo autologous stem cell transplantation, their prognosis remains poor . The greRecently, r/r patients have gained the access to adoptive therapy using genetically engineered T cells with chimeric antigen receptor (CAR-T cells) targeting DLBCL-expressed surface antigens. Particularly promising results of this treatment strategy have been observed in the case of CD19-CARs. Three of them, i.e., axicabtagene ciloleucel , lisocabtagene maraleucel , and tisagenlecleucel  are licensed to treat DLBCL patients .However, years of employment of rituximab as a core of first-line treatment and recent observations on CAR-T cell therapy clearly demonstrate that the use of immunotherapy invariably leads to the induction of resistance . A deep Targeting CD20 antigen using rituximab, a monoclonal antibody (mAb) approved by the FDA in 1997, remains the most widely used form of immunotherapy in oncology. CD20 is a membrane protein found on the surface of normal and neoplastic B lymphocytes, but not on hematopoietic progenitors, which makes it a suitable target for mAbs . ExperieThe cytotoxic effect of rituximab is exerted through several mechanisms, relying on the activation of anti-tumor immune response . TheThe results of preclinical studies in vitro and in vivo in animal models as well as observations from the clinical settings suggest that all the above-mentioned mechanisms contribute to the overall efficacy of rituximab. However, it seems that the influence of single effector mechanisms of rituximab may vary depending on the tumor type.dim NK cells with higher levels of FcγRIII receptor (CD16) G allele, which most probably correlates with higher serum levels of C1q and thus higher complement activity, have a significantly shorter time to progression than A carriers [C1qA[276]G allele had not only shorter PFS but also decreased OS and CR rates [The engagement of CDC in the clinical success of rituximab in NHL is a matter of controversy, as it has been suggested that complement activation can be detrimental to antibody immunotherapy in murine lymphoma models. It has been demonstrated that breakdown products of the complement component C3 block rituximab activity and render NK cells less active in ADCC . In linecarriers . An analCR rates . NeverthThe binding of rituximab to CD20 induces the translocation of the antibody–antigen complex to the lipid rafts , highly Already 10 years after rituximab introduction to the clinic, two large studies on r/r DLBCL patients clearly demonstrated that rituximab used as the first-line treatment leads to substantial alterations in the biology of malignant cells and makes them resistant to subsequent salvage regimens ,110. EarTo better understand the mechanisms of resistance, cell line models have been generated by Czuczman et al. , Barth eFirst of all, they enabled to determine that CD20 downregulation occurs following exposure to rituximab is regulated on both transcriptional and posttranslational levels ,113. SecResistance to rituximab and R-CHOP results as a consequence of molecular alterations of both target malignant cells, effector immune cells, and the influence of tumor microenvironment. One of the most immediate causes of resistance to anti-CD20 mAbs are decreased CD20 levels ,115. YetCD20 levels have been shown by several groups to correlate with the efficacy of rituximab (reviewed in ). ExperiThe biological function of CD20 is still unclear despite years of extensive research. CD20 is described as a component of a store-operated calcium entry pathway activated by B-cell receptor (BCR) . Until nThe mechanisms of CD20 regulation and potential compounds that may be combined with rituximab in order to increase its efficacy by inducing CD20 upregulation are elegantly reviewed in ,131. A gIgG1 gene strongly augmented the ability of some Abs to induce CDC and ADCC against lymphoma cell lines and primary CLL samples [Recently, it has been demonstrated that Fc fragments of IgG antibodies form ordered Ab-hexamers following antigen binding . The int samples . Since b samples .Given the mentioned poor prognosis following the first-line treatment with rituximab in relapsed patients, the mechanisms of chemoresistance have also been thoroughly studied in the models of RRCLs. All the RRCLs are resistant to cisplatin, doxorubicin, paclitaxel, and vincristine due to decreased expression of the Bcl-2 family proteins, i.e., Bax, Bak, Bcl-2, and increased expression of Bcl-xL and Mcl-1 ,136. TheRRCLs are also characterized by profound metabolic changes. It has been demonstrated that the acquirement of rituximab resistance is associated with deregulation in glucose metabolism caused by overexpression of hexokinase II (HKII), which performs dual functions of promoting glycolysis as well as inhibiting mitochondrial-mediated apoptosis . These dRecently, more attention has been given to the role of the tumor environment in promoting tumor progression and influencing the clinical response to treatment . In NHL Although bone marrow infiltration is not common in DLBCL, the studies of this milieu in the context of therapy efficacy need to be developed as bone marrow-derived MSCs have been shown to promote the growth and primary resistance of DLBCL cell lines to rituximab by secreting IL-6 and elevating IL-17A levels in in vitro settings . IncreasConversely, rituximab has been shown to promote long-time response due to its vaccinal effect as demonstrated in EL4-huCD20 murine models of lymphoma that were resistant to tumor expansion at rechallenge with RTX . FurtherAs DLBCL cells predominantly reside in lymph nodes, the stromal factors implicated in the organization of extracellular matrix (ECM) and the cells of innate immunity, especially macrophages, dendritic cells (DCs), and NK cells are involved in the physiopathology and drug response of DLBCL . A studyIn light of the above, modifying the tumor environment with immunomodulatory drugs seems a rational solution. One of such drugs is lenalidomide, currently approved by FDA for MCL treatment . In an iDespite the success of rituximab as a safe and efficient therapeutic option, the incidence of resistance encouraged searching for other molecular targets for immunotherapies in the management of DLBCL . These nA way to improve the efficacy of treatment response is to employ dual antigen targeting with bispecific Abs (bsAbs) to stimulate a more potent anti-tumor response, increase binding specificity and overcome the low efficacy of single-epitope targeting. Moreover, the inclusion of single-chain variable fragment (scFv) of an anti-CD16 Ab into bsAb constructs has been employed to enhance CD16-positive effector cell recruitment . In addiAntibody–drug conjugates are composed of specific mAbs linked to cytotoxic agents, the so-called free payload, that exert their cytotoxic action upon internalization . Since tSeveral specific markers of B cells that may be used as targets for ADCs have been identified in in vitro studies, i.e., CD19, CD20, CD21, CD22, CD72, CD79b, and CD180 . By now,MDR1 gene [Based on in vitro studies and xenograft models of repeated exposure to consecutively increasing concentrations of ADCs, several leading cellular mechanisms, by which the acquired resistance to ADCs occurs were described, each elegantly reviewed by Loganzo et al. . The firDR1 gene . The expDR1 gene .Several attempts are undertaken to enhance the therapeutic potential of ADCs . AlteratIn recent years, considerable progress in hematooncology has been achieved by developing adoptive therapies based on T cells modified with chimeric antigen receptors (CARs) ,193. CARHowever, almost half of patients with high-grade lymphomas progress after CD19 CAR-T cell therapy and require additional treatment . The mecIn ALL, antigen loss is one of the most frequent reasons for relapse after CD19 CAR-T therapy. Disease relapse can occur by 12 months after infusion of CAR-T cells in up to 50% of patients . RelapseRB1 or RHOA that according to the authors correlate with exhausted immune microenvironments and resistance/poor response to CAR therapy [Thus far, two main mechanisms of antigen loss were described in ALL–antigen escape and lineage switch. In the first case, after remission in response to CD19 CAR-T therapy, patients lacking surface CD19 antigen relapse but with a phenotypically similar disease. In contrast, lineage switch describes the opposite situation, when relapse is a phenotypically different malignancy, but genetically similar . That oc therapy .CD19 loss after CAR-T therapy in ALL can be accompanied by the downregulated expression of some surface antigens, such as CD20 and CD22 . A similHematological malignancies derive from immune cells and are surrounded by other components of the immune system. This provides many opportunities to interact with the tumor environment, locally transported antibodies, and the complement system. These characteristics shape unique conditions for the use of immunotherapy, which in the last two decades has become the mainstream for many types of hematological malignancies, including the most frequent one—DLBCL.Despite extensive research on novel therapeutic options, R-CHOP combination remains the core of therapy in DLBCL. While the frequency of primary resistance to R-CHOP is relatively low (approx. 10%), secondary resistance acquired in the course of therapy leads to relapse in around one-third of patients . The sit


In the UK, injectable medicines are often prepared and administered by nurses following the Injectable Medicines Guide (IMG). Our earlier study confirmed a higher frequency of correct administration with user-tested versus standard IMG guidelines. This current study aimed to model the cost-effectiveness of user-testing.The costs and cost-effectiveness of user-testing were explored by modifying an existing probabilistic decision-analytic model. The adapted model considered administration of intravenous voriconazole to hospital inpatients by nurses. It included 11 error types, their probability of detection and level of harm. Model inputs (including costs) were derived from our previous study and other published data. Monte Carlo simulation using 20,000 samples (sufficient for convergence) was performed with a 5-year time horizon from the perspective of the 121 NHS trusts and health boards that use the IMG. Sensitivity analyses were undertaken for the risk of a medication error and other sources of uncertainty.The net monetary benefit at £20,000/quality-adjusted life year was £3,190,064 : −346,709 to 8,480,665), favouring user-testing with a 96% chance of cost-effectiveness. Incremental cost-savings were £240,943 , also favouring user-tested guidelines with a 99% chance of cost-saving. The total user testing cost was £6317 (95% CrI 6012–6627). These findings were robust to assumptions about a range of input parameters, but greater uncertainty was seen with a lower medication error risk.User-testing of injectable medicines guidelines is a low-cost intervention that is highly likely to be cost-effective, especially for high-risk medicines. Each yen = 67, 48%) than with current guidelines   1.68–3.60). As medication errors can cause harm and thus increase health service costs [In the UK, nurses prepare and administer most intravenous doses on hospital wards. In over 120 hospital organisations, they use the NHS  Injectable Medicines Guide (IMG) website as a source of written information on how to do this for each dose. Two linked studies have shown that nurses using a user-tested version of the IMG were more likely to correctly prepare and administer an intravenous medicine, and did this more quickly, than nurses using the current version , 12. Usece costs , there iEconomic decision analytic models can synthesise and extrapolate evidence from randomised trials and other sources to compare the expected costs and consequences of different decision options . The aim2There are numerous types of medication error and therefore many potential pathways for the development of harmful effects. Economic models of interventions to reduce medication errors therefore usually consider general categories of harm severity, rather than specific pathways. To do this, several parameters must be determined, including the number of errors with and without the intervention, the likelihood of detection of errors before administration, the level of harm produced, the cost of treating such adverse effects, and the intervention costs. In addition, to achieve our second objective, we also needed to consider the effects of harmful errors of different levels of severity on patients’ quality of life.Three previous economic models of interventions to reduce medication errors were identified . The proWe followed the Consolidated Health Economic Evaluation Reports Standards .2.1The IMG is currently used by nurses preparing medicines in the hospitals of 121 NHS trusts and health boards throughout the UK . These organisations also share resources to maintain and develop the guide. We therefore performed analysis from the perspective of the NHS payer. The population was hospital inpatients receiving intravenous voriconazole in all 121 hospital trusts and health boards within our perspective see .2.2The intervention was the user-testing process to revise the design of the IMG . This in2.3All outcomes were calculated across the time horizon  for the We calculated the mean decrease in the total number of medication errors that cause moderate-severe patient harm  with the user-tested IMG . We alsWe calculated the incremental cost-saving (ICS) as the difference between health service costs with the current IMG and health service costs with the user-tested IMG. This included the costs associated with medicines administration (nursing time), medication errors , and the user-testing intervention. Due to the complexity of including insured costs, we did not consider litigation costs as they represent only 0.5% of the total cost of NHS medication errors .We assumed that quality of life would be decreased by a medication error, so we assessed the benefits of user-testing using QALY decrements following a medication error. User-tested guidelines would therefore be more beneficial than current guidelines if they result in a smaller QALY decrement. We evaluated cost-effectiveness by calculating net monetary benefit (NMB). NMB represents the additional value in monetary terms generated by user-testing, so a positive NMB would indicate that user-testing is optimal. The NMB was calculated as the difference between expected incremental benefits  and expected incremental costs. We assessed uncertainty in the user-testing decision by reporting 95% credible intervals and the probability of a positive ICS or NMB, as well as cost-effectiveness acceptability curves.2.4We used a 5-year time horizon. As user-testing results in changes to the design of the IMG, its effects persist and further user-testing is not required unless there are changes in practice or training. We considered 1- and 10-year time horizons in sensitivity analyses. We assumed that user-testing took place once at the start of the time horizon, so user-testing costs were not discounted. An annual discount rate of 3.5% was applied to other costs and QALYs .2.5The Karnon et al. decision tree model  consider2.6Data sources used to populate the model to estimate the number of medication errors and pADEs are described in the following sections. Input parameter values and distributions for probabilistic analysis are presented in 2.6.1We used the frequency of errors observed in our previous clinical study (section 1.0)  to calcuWe also conducted a structural sensitivity analysis considering the effects of multiple errors per dose, assuming that their costs and QALY decrements were additive  for the 2018 price year.We established face validity by discussion with an advisory group  and within the research team . We systematically checked internal validity by assessing core calculations and applying a series of logical checks to ensure the direction of predictions was consistent. External and cross validity were more difficult to establish, as there are few comparable studies.3The results from the base-case analysis for the whole cohort of patients within an NHS payer perspective are presented in 4This paper reports the development of a model to assess the cost-effectiveness of user-testing injectable medicines guidelines for use by nurses preparing intravenous medicines. The results suggest that for high-risk situations (e.g. an unfamiliar drug), user-testing reduces the number of pADEs and is both cost-saving and cost-effective. These findings were robust to many sources of uncertainty. For lower-risk situations, there is greater uncertainty about these outcomes, especially if user-testing is relatively less effective, although there is still > 70% probability that user-testing is cost-effective. Although these findings are specific to the UK, they are likely to be applicable in any setting that uses similar intravenous medicines administration systems, as user-testing is a relatively inexpensive intervention and a reduction in pADEs is likely to result in large cost reductions.There are no other published economic evaluations of user-testing for improving medicines safety. The cost-effectiveness of several other hospital medicines safety interventions has been assessed. We based the model described in this study on one developed by Karnon et al., who used this approach to evaluate the cost-effectiveness of three medicines safety interventions: computerised physician order entry (CPOE), additional ward pharmacists, and bar coding of medicines . In contThese comparisons emphasise that user-testing is a relatively low-cost patient safety intervention, with a small initial cost and little requirement for repetition or maintenance (unlike training or CPOE for example). The present study assumed that user-testing was carried out by NHS-employed pharmacists. However, utilising the expertise of user-testing specialists, with the input of specialists in information writing and design, may produce even more effective guidelines. The time to complete the process might also be reduced, but with some increased costs. In addition, the present study describes an ‘intensive’ user-testing process, where the guideline for one medicine was tested with 30 nurses. However, some of the findings from this process are applicable to the IMG guidelines for all other medicines. Such learning will reduce the overall amount of testing required to improve the guidelines for all 350 medicines in the IMG, thus further improving the cost-effectiveness of user-testing.4.1One limitation of our study is that the modelled results draw on an in situ simulation rather than the investigation of actual patient care . This ma4.2These results suggest that the IMG should adopt the process of user-testing to produce guidelines for high-risk medicines. Evaluation of the impact of this change could inform a similar decision in relation to lower-risk medicines. As user-testing is a low-cost intervention and the costs of pADEs are relatively much higher, this approach should also be considered for other types of written guidelines for health professionals, including those both related and unrelated to medicines. Economic evaluation is an important tool to support the introduction of medicines safety interventions but is currently limited by lack of consensus and rigour in relation to how the costs and consequences of medication errors should be calculated . Future 5There is a high probability that user-testing injectable medicines guidelines for nurses preparing intravenous medicines in clinical areas is both cost-effective (96%) and cost-saving (99%) for high-risk medicines. The probability that user-testing is cost-effective and cost-saving is reduced for lower-risk medicines, but is still 71% and 80% (respectively), as user-testing is a low-cost intervention.https://doi.org/10.1007/s40258-021-00675-z.The online version contains supplementary material available at Online Supplementary Material


Following skeletal muscle injury (SMI), from post-injury reaction to repair consists of a complex series of dynamic changes. However, there is a paucity of research on detailed transcriptional dynamics and time-dependent marker gene expression in the early stages after SMI. In this study, skeletal muscle tissue in rats was taken at 4 to 48 h after injury for next-generation sequencing. We examined the transcriptional kinetics characteristics during above time periods after injury. STEM and maSigPro were used to screen time-correlated genes. Integrating 188 time-correlated genes with 161 genes in each time-related gene module by WGCNA, we finally identified 18 network-node regulatory genes after SMI. Histological staining analyses confirmed the mechanisms underlying changes in the tissue damage to repair process. Our research linked a variety of dynamic biological processes with specific time periods and provided insight into the characteristics of transcriptional dynamics, as well as screened time-related biological indicators with biological significance in the early stages after SMI. Then, we examined the injured rats in groups at seven time points after SMI . To confTo objectively describe and confirm the processes of pathological changes in the early period after SMI based on transcription profiles, we examined genes with obvious changes in expression, Gene Ontology (GO) terms, and pathways during the early phases of the post-injury response. In this study, we obtained raw next-generation sequencing data from 60 samples  . Then wep-values. Correlations with time and the biological significance of the 23 meaningful modules were visualized in a module-trait relationship heat map  corresponding to the major contributions of the expression profile) . Due to the unequal numbers of genes in each selected module, the numbers of biologically meaningful GO terms chosen differed among modules  exhibited a time-varying pattern. From the number of enriched GO terms, we inferred that inflammation predominated from 4 to 12 h after injury. Similarly, in the immune process stage, most immune-related GO terms were enriched in the “12” module. By contrast, autophagy was only enriched in the “24” module . In addiTaken together, the findings outlined above demonstrated the temporal variation in GO terms enriched in each HCr module during the entire course of the SMI repair process. The changes in gene expression profiles revealed the basic biological processes and pathological changes occurring during the SMI repair process.Similar to GO term enrichment analysis, the entire course of the SMI repair process could be roughly divided into stages associated with damage, the repair process, and signal regulation based on KEGG pathway analysis . We alsoIn this turn, temporal variation in the active biological processes and pathways after injury were precisely regulated by some aspects of regulation-related KEGG pathways, especially during the initial period of the SMI repair process  in each HCr module objectively reflected the transcriptional dynamics as observed in gene expression profiles and the biological significance of the HCr modules .To further confirm the HCr module selected previously to represent each period, and to study the expression of representative functional genes in each HCr module, we screened the top 30 high-connectivity genes in each HCr module based on the results outlined above. First, we entered all co-expressed genes in each module  into theWe performed detailed enrichment analyses of GO terms representing biological processes and KEGG pathways for these 161 high-connectivity co-expressed genes . The bubNext, to better show the significance of these genes in each HCr module, a heat map of the top high-connectivity co-expressed genes was produced, which clearly revealed some genes were more highly expressed at specific time points after injury, indicating the relevance of the corresponding HCr module to each time point . The expHaving analyzed the HCr modules identified by WGCNA and expanded on the transcriptional dynamics of the gene expression profiles in all time periods covering the early stages of the SMI repair process, we focused on the time-dependent variation in gene expression. Based on the data analysis workflow , we idenAs described above, raw clean data on the experiment-wide gene expression profiles were obtained from 53 samples and 20,662 genes . The resNext, to study the temporal variation in the expression of DEGs at different time points, we performed pattern analysis using STEM software  was shown in Comparison of the gene expression profiles between the control group and the experimental groups revealed that there were 540 DEGs expressed in common in the seven experimental groups. The distribution of all DEGs in 7 experiment groups  selected by STEM. To obtain marker genes fully reflecting temporal variation during the SMI repair process, we extracted the union set from the total of genes and identified 188 genes for further in-depth analysis .This set of 188 genes included marker genes exhibiting time-dependent variation in expression and genes with dynamic expression during the early period after SMI. The circular heat map in p-value (p < 0.01) and identified 52 GO terms  and KEGG pathway analyses. These 188 genes were associated with 737 GO terms and 48 KEGG pathways. In the GO term enrichment analysis, we selected GO terms based on the GO terms . The selGO terms .In summary, the results suggested that these genes not only reflected time-dependent changes but also included hub genes and core regulatory genes participating in the many complex processes involved in SMI repair.Having illustrated the characteristics of the transcriptional dynamics and time-dependent gene expression variation, in this section, we further illustrate the possible connection between transcriptional dynamics that reflect biological processes and time-dependent marker genes to identify potential biomarkers that will allow us to elucidate the correlations between temporal changes in gene expression and the biological processes that occur in the early period after SMI. In addition, we verified our results based on morphological and immunohistochemical analyses.First, we identified the genes in common between the 161 high-connectivity co-expressed genes (eigengenes) in the HCr modules and the 188 time-dependent marker genes. The results indicated that there were 18 genes common to the two gene sets . Next, wThese observations indicated that the transcriptional dynamics of the co-expressed genes in the HCr modules were closely related to the time-dependent marker genes through these 18 genes. In addition, based on the integration of transcriptional dynamics and the time-dependent marker genes, some of the hub genes may reflect temporal gene-expression changes associated with biological processes occurring in the early stage following SMI.Next, to intend confirm the process of inflammation during the early period after SMI, we labeled neutrophils using an anti-MPO primary antibody. The results of immune-histochemical analysis suggested that, in comparison with the normal control group, with the development of inflammation, the degree of neutrophil infiltration and the neutrophil distribution varied in a characteristic manner during the early period (0–24 h) after SMI .Taken together, the results of the histo-morphological and immune-histochemical analyses reflected the characteristics of the SMI repair process.SMI repair is a continuous and dynamic biological process  will change during the occurrence of diseases or specific biological processes driven  in skeletal muscle tissue biosystem. Interestingly, during 24–48 h, especially at 48 h after injury, more intramedular eigengenes were screened out , which mFor another part, during the damage-repair process post-SMI, the dynamic changes based on transcriptional kinetics and data on the time-specific and time-related gene screening obtained via the application of time-related algorithms have high reference value, especially for forensic research on wound age estimation. Additionally, we have information on methods for the examination of initial tissue morphology  to regulate reprogramming of pro-inflammatory macrophages (M1) to anti-inflammatory macrophages (M2). The transformation of macrophage polarization from inflammatory (M1) to anti-inflammatory (M2) phenotype has significant significance for the regeneration of damaged tissues, the treatment of inflammatory diseases and the remission of autoimmune diseases ; Regular Diet Services]. Sixty-four male Sprague Dawley rats were divided randomly into eight groups  once their body weight reached 240 g (±20 g)  and housed in environmentally enriched ventilated cages under specific pathogen-free conditions at the Provincial Key Laboratory of Forensic Medicine, Shanxi Medical University  under a 12h light/dark cycle with  (±20 g) . The ani260/OD280 ratios within the range 1.8–2.2 and an RNA integrity number > 7.0 were used in the following experiments. Samples not meeting these criteria were excluded from the analysis (n = 4).Frozen muscle samples (~50 mg each) were pulverized in liquid nitrogen with an RNase-free mortar and pestle, and then dissolved using RNAiso Plus 9108 (Takara Bio) in accordance with the manufacturer's instructions. The concentration (ng/mL) and purity of the freshly extracted total RNA were measured using a microplate reader . The quality of the extracted total RNA was determined using spectrophotometry . The concentration of total RNA was determined using fluorometry  and the integrity of total RNA was determined with an Agilent 2100 Nano 6000 Assay kit on an Agilent 2100 Bioanalyzer system. Only RNA samples with ODSixty samples were submitted to Illumina for library preparation using a NEBNext Ultra RNA Library Prep Kit  and fluorometer  to build and preliminarily quantify the library. The library was then quantified accurately  and the insert size of the library was determined . The library of 60 samples was then subjected to pair-end 150-bp sequencing, aiming for coverage of 40–60 M reads, using a HiSeq PE Cluster Kit v4-cBot-HS on the HiSeq-2500 platform (Illumina). The off-machine data were converted into raw sequence reads after base recognition using bcl2fastq2 software  > 1 or < −1 and adjusted p < 0.05 corrected for multiple testing using the Benjamini–Hochberg method   , are critical for the maintenance of overall network stability. In WGCNA, intra-module connectivity and correlations with module eigengenes were used to select hub genes without any statistical criteria    and control group (n = 8) were harvested and placed in 10% neutral buffered formalin for 24 h. The muscle tissue was rinsed and flushed with phosphate-buffered saline, gently squeezed to remove non-adherent bacteria, and embedded in paraffin. Then, longitudinal sections 5 μm thick were cut for histological observations and immune-histochemical processing. Histological sections for each time point were stained with H&E to observe the progression from damage to repair in the early period following SMI.The injured limbs from the experimental groups  was used with a Metal Enhanced DAB Chromogenic Kit  to stain infiltrating neutrophils. A secondary anti-mouse/rabbit horseradish peroxidase immune-globin G antibody  was used to identify sites of inflammation. Sections were counterstained with hematoxylin.Following staining, the slides were imaged using a Tissue Fax Plus 2000 slide scanner (Tissuen Gnostics) at a magnification of 40× (H&E staining) or 20× .https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE171243, Repository: GEO Accession number: GSE171243.The RNA-seq raw data upload information link, repositories and accession number in the article are listed as follows: Information link: The animal study was reviewed and approved by Animal Ethics Committees of Shanxi Medical University [reference number 2016LL151].JS and YW conceived and supervised the project. KR, JS, and QD designed the research. LiangW, NL, LD, LianglW, and YT performed the animal and RNA experiments. LianglW and GA performed data and bioinformatics analyses. JC, LiangW, and KR performed the HE and IHC staining. LianglW and QJ performed the figures on this paper. JS, YW, QD, KR, and NL interpreted the results. KR, JC, and JS wrote the paper. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.