Using WhatsApp and Facebook Online Social Groups for Smoking Relapse Prevention for Recent Quitters: A Pilot Pragmatic Cluster Randomized Controlled Trial.

BACKGROUND: Quit attempters often have episodes of smoking relapse before they eventually quit. Interactive text messaging through mobile phones has been shown to increase abstinence. This service can be potentially applied on the platform of a social networking service to help quitters maintain abstinence. OBJECTIVE: Our aim was to determine if the group discussion and reminders via the WhatsApp or Facebook social group were effective to prevent smoking relapse in quitters who had stopped smoking recently. METHODS: This was a single-blinded, parallel, 3-arm pilot cluster randomized controlled trial allocating recent quitters, who had completed an 8-week treatment and reported abstinence for at least 7 days, to WhatsApp (n=42), Facebook (n=40), and a control group (n=54). The 2 intervention groups participated in a 2-month online group discussion with either WhatsApp or Facebook moderated by a trained smoking cessation counselor and received a self-help booklet on smoking cessation. The control group only received the booklet. The primary outcome was the 2- and 6-month relapse rates, defined as the proportion of participants who smoked at least 5 cigarettes in 3 consecutive days. RESULTS: Fewer participants in the WhatsApp group (17%, 7/42) reported relapse than the control group (42.6%, 23/54) at 2-month (OR 0.27, 95% CI 0.10-0.71) and 6-month (40.5%, 17/42 vs 61.1%, 33/54; OR 0.43, 95% CI 0.19-0.99) follow-ups. The Facebook group (30.0%, 12/40) had an insignificantly lower relapse rate than the control group (42.6%, 23/54) at 2-month (OR 0.58, 95% CI 0.24-1.37) and 6-month (52.5%, 13/40 vs 61.1%, 33/54; OR 0.70, 95% CI 0.31-1.61) follow-ups. The WhatsApp social groups had more moderators' posts (median 60, IQR 25 vs median 32, IQR 7; P=.05) and participants' posts (median 35, IQR 50 vs median 6, IQR 9; P=.07) than their Facebook counterparts, but the difference was insignificant. CONCLUSIONS: The intervention via the WhatsApp social group was effective in reducing relapse probably because of enhanced discussion and social support. Inactive discussion in the Facebook social group might have attributed to the lower effectiveness. CLINICALTRIAL: Clinicaltrials.gov NCT02007369; https://clinicaltrials.gov/show/NCT02007369 (Archived by WebCite® at http://www.webcitation.org/6c3RbltQG).

Automatic detection of cardiac conditions from photos of electrocardiogram captured by smartphones.

BACKGROUND: Researchers have developed machine learning-based ECG diagnostic algorithms that match or even surpass cardiologist level of performance. However, most of them cannot be used in real-world, as older generation ECG machines do not permit installation of new algorithms. OBJECTIVE: To develop a smartphone application that automatically extract ECG waveforms from photos and to convert them to voltage-time series for downstream analysis by a variety of diagnostic algorithms built by researchers. METHODS: A novel approach of using objective detection and image segmentation models to automatically extract ECG waveforms from photos taken by clinicians was devised. Modular machine learning models were developed to sequentially perform waveform identification, gridline removal, and scale calibration. The extracted data were then analysed using a machine learning-based cardiac rhythm classifier. RESULTS: Waveforms from 40 516 scanned and 444 photographed ECGs were automatically extracted. 12 828 of 13 258 (96.8%) scanned and 5399 of 5743 (94.0%) photographed waveforms were correctly cropped and labelled. 11 604 of 12 735 (91.1%) scanned and 5062 of 5752 (88.0%) photographed waveforms achieved successful voltage-time signal extraction after automatic gridline and background noise removal. In a proof-of-concept demonstration, an atrial fibrillation diagnostic algorithm achieved 91.3% sensitivity, 94.2% specificity, 95.6% positive predictive value, 88.6% negative predictive value and 93.4% F1 score, using photos of ECGs as input. CONCLUSION: Object detection and image segmentation models allow automatic extraction of ECG signals from photos for downstream diagnostics. This novel pipeline circumvents the need for costly ECG hardware upgrades, thereby paving the way for large-scale implementation of machine learning-based diagnostic algorithms.

Mycorrhizal colonization status of lowland rice (Oryza sativa L.) in the southeastern region of China.

The accumulation, distribution, and speciation of contaminants, such as arsenic, in rice can be affected by soil microorganisms such as arbuscular mycorrhizal fungi (AMF). As a potential measure to control contaminant acquisition in rice, the status and performance of AMF in the field need to be investigated. Root samples of rice plants were collected in seven different cities in Guangdong, Jiangxi, Hubei, and Jiangsu Provinces in China in order to investigate the colonization rate of AMF. The total DNA of the roots was extracted, followed by PCR and sequencing, and further confirmed the existence of AMF. The highest colonization rates (19.5 ± 7.2%) were observed in samples from Huizhou City, Guangdong Province. Sequences of ribosomal DNA derived from Pingtan (PT) and Shuikou (SK) in Huizhou shared a similarity of 73 and 86% to Glomus cf. clarum Att894-7 (FM865542) and "uncultured fungus" (EF434122.1), respectively. The moisture tolerance of the AMF from different sources was tested by subjecting to different levels of water content in the soil. Only AMF from PT, SK, and LJ colonized rice under a condition of 100% of the soil water holding capacity (WHC), but not those isolated from upland plants. The AM colonization rate could be governed by the lighting conditions and temperature. AMF isolated in paddy fields has been shown to have more tolerance to moisture than other upland species. Radial oxygen loss (species and stress dependent) could be an essential factor influencing the colonization rate and requires more investigation.

Arsenite transporters expression in rice (Oryza sativa L.) associated with arbuscular mycorrhizal fungi (AMF) colonization under different levels of arsenite stress.

As a silicon hyperaccumulator, lowland rice takes up higher levels of As than many other plants due to silicic acid and arsenite sharing the same transporters (Lsi1 and Lsi2). Glomus intraradices (AH01) was inoculated to rice under different arsenite concentrations (0, 2 and 8 µM) in order to investigate the interactions between arbuscular mycorrhizal fungus and rice on the accumulation of arsenite. The relative mRNA expressions of Lsi1 and Lsi2 resulted in a down-regulating trend in mycorrhizal plants. Under 2 µM arsenite treatments, Lsi1 and Lsi2 were significantly decreased, by 0.7-fold (P<0.05) and 0.5-fold (P<0.01), respectively, in mycorrhizal plants when compared with non-mycorrhizal plants. This led to the decrease of arsenite uptake per unit of root dry mass. No organic As species were detected in both roots and shoots. The As(III)/As(V) ratios indicated that mycorrhizal plants immobilized most of the arsenite proportion in the roots and prevented its translocation from the roots to the shoots.