Espécies e serovariantes de agentes enteropatogênicos associados com diarréia aguda em Rosario, Argentina

We report the most frequent species and serovars of enteropathogenic organisms in Rosario from 1985 to 1993. Enteropathogenic Escherichia coli was the most prevalent agent affecting 144/570 (25.2%) children; 0111 represented 41.8%, 055: 13.6%, 0119: 12.7%. Among enterotoxigenic E. coli (ETEC) the most frequent were ETEC-ST 0128:H21 and 0153:H45. Shigella spp were isolated in 8.8%; S.flexneri: 7%, principally type 2 (59.5%); S. sonnei: 1.6%, and S. dysenteriae type 2: 0.2%. Campylobacter spp were found in 6.1% of patients; C.jejuni: 4.6%; C. coli: 1.4% and C. lari: 0.2%; except groups 0 13,50 and 0 4 (2 cases each), no predominant serogroups were found. Salmonella was isolated in 2.8% of cases, being the predominant serovar S. typhimurium until 1986, but a dramatically increase of cases due to S. enteritidis was observed since 1987. There was 1.9% of Aeromonas spp and 2 cases due to Vibrio cholerae non 0-1. No Yersinia was found. In patients with gastroenteritis due to Shigella, Campylobacter, Salmonella, or EPEC as the unique pathogen, leukocytes were observed in the faeces in 70%, 50%, 20%, and 10% of cases respectively.Apresentamos as espécies e serovars mais frequentes dos microorganismos enteropatógenos entre 1985 e 1990 em Rosario. Escherichia coli enteropatogênica (EPEC) foi a que predominou, afetando 144/570 (25,2%) crianças; 0111 representou 41,8%, 055 13,6%, 0119 12,7%. Entre as E. coli enterotoxigênicas (ETEC), ETEC-ST 0128:H21 e 0153:H45 foram as mais frequentes. Entre os 570 pacientes, Shigella spp. foi diagnosticada em 50 (8,8%); S. flexneri 7%, principalmente do tipo 2 (59,5%), S. sonnei 1,6% e S. dysenteriae tipo 2 (1%). Foram encontrados Campylobacter spp em 6,1% dos pacientes; C. jejuni 4,6%, C. coli 1,4% e C. lari 0,2%; exceto os grupos 0 13/50 e 0 4 (dois de cada um), não foram encontrados serogrupos predominantes. Salmonella foi encontrada em 2,8% dos casos, sendo o serovar S. typhimurium o predominante até 1986, mas desde 1987, foi observado um aumento importante de casos por S. enteritidis. Houve 1,9% de Aeromonas spp e dois casos por Vibrio cholerae não 01. Não se encontrou Yersinia spp. Nos pacientes com gastroenteritis por Shigella, Campylobacter, Salmonella e EPEC como único patógeno, foram observados leucocitos nas feses de 70%, 50%, 20% e 10% dos casos respectivamente

Automated calibration of somatosensory stimulation using reinforcement learning

Abstract Background The identification of the electrical stimulation parameters for neuromodulation is a subject-specific and time-consuming procedure that presently mostly relies on the expertise of the user (e.g., clinician, experimenter, bioengineer). Since the parameters of stimulation change over time (due to displacement of electrodes, skin status, etc.), patients undergo recurrent, long calibration sessions, along with visits to the clinics, which are inefficient and expensive. To address this issue, we developed an automatized calibration system based on reinforcement learning (RL) allowing for accurate and efficient identification of the peripheral nerve stimulation parameters for somatosensory neuroprostheses. Methods We developed an RL algorithm to automatically select neurostimulation parameters for restoring sensory feedback with transcutaneous electrical nerve stimulation (TENS). First, the algorithm was trained offline on a dataset comprising 49 subjects. Then, the neurostimulation was then integrated with a graphical user interface (GUI) to create an intuitive AI-based mapping platform enabling the user to autonomously perform the sensation characterization procedure. We assessed the algorithm against the performance of both experienced and naïve and of a brute force algorithm (BFA), on 15 nerves from five subjects. Then, we validated the AI-based platform on six neuropathic nerves affected by distal sensory loss. Results Our automatized approach demonstrated the ability to find the optimal values of neurostimulation achieving reliable and comfortable elicited sensations. When compared to alternatives, RL outperformed the naïve and BFA, significantly decreasing the time for mapping and the number of delivered stimulation trains, while improving the overall quality. Furthermore, the RL algorithm showed performance comparable to trained experimenters. Finally, we exploited it successfully for eliciting sensory feedback in neuropathic patients. Conclusions Our findings demonstrated that the AI-based platform based on a RL algorithm can automatically and efficiently calibrate parameters for somatosensory nerve stimulation. This holds promise to avoid experts’ employment in similar scenarios, thanks to the merging between AI and neurotech. Our RL algorithm has the potential to be used in other neuromodulation fields requiring a mapping process of the stimulation parameters. Trial registration: ClinicalTrial.gov (Identifier: NCT04217005

Automated calibration of somatosensory stimulation using reinforcement learning

Background: The identification of the electrical stimulation parameters for neuromodulation is a subject-specific and time-consuming procedure that presently mostly relies on the expertise of the user (e.g., clinician, experimenter, bioengineer). Since the parameters of stimulation change over time (due to displacement of electrodes, skin status, etc.), patients undergo recurrent, long calibration sessions, along with visits to the clinics, which are inefficient and expensive. To address this issue, we developed an automatized calibration system based on reinforcement learning (RL) allowing for accurate and efficient identification of the peripheral nerve stimulation parameters for somatosensory neuroprostheses. Methods: We developed an RL algorithm to automatically select neurostimulation parameters for restoring sensory feedback with transcutaneous electrical nerve stimulation (TENS). First, the algorithm was trained offline on a dataset comprising 49 subjects. Then, the neurostimulation was then integrated with a graphical user interface (GUI) to create an intuitive AI-based mapping platform enabling the user to autonomously perform the sensation characterization procedure. We assessed the algorithm against the performance of both experienced and naïve and of a brute force algorithm (BFA), on 15 nerves from five subjects. Then, we validated the AI-based platform on six neuropathic nerves affected by distal sensory loss. Results: Our automatized approach demonstrated the ability to find the optimal values of neurostimulation achieving reliable and comfortable elicited sensations. When compared to alternatives, RL outperformed the naïve and BFA, significantly decreasing the time for mapping and the number of delivered stimulation trains, while improving the overall quality. Furthermore, the RL algorithm showed performance comparable to trained experimenters. Finally, we exploited it successfully for eliciting sensory feedback in neuropathic patients. Conclusions: Our findings demonstrated that the AI-based platform based on a RL algorithm can automatically and efficiently calibrate parameters for somatosensory nerve stimulation. This holds promise to avoid experts’ employment in similar scenarios, thanks to the merging between AI and neurotech. Our RL algorithm has the potential to be used in other neuromodulation fields requiring a mapping process of the stimulation parameters. Trial registration: ClinicalTrial.gov (Identifier: NCT04217005)ISSN:1743-000