Image caption, McConnell released a photo of himself with his wife alongside Sunday's statement US Senator Mitch McConnell says he will not be returning to the Senate quite yet after suffering from a fall and a mild case of pneumonia. It is the first statement from the 84-year-old Kentucky Republican after weeks of speculation about his health, following his admission to hospital in mid-June. A photo was released by his office, in addition to the statement, which shows McConnell with his wife holding what appeared to be Sunday's Washington Post newspaper. He said he was briefly unconscious after his fall and taken to hospital, where he had submitted to every test they can think of to help figure out what caused this incident. My doctors have confirmed that I didn't break any bones or suffer a concussion.

'This is an apartheid regime' Does Trump have real leverage over Netanyahu? Hospital workers wounded in Israeli drone attack on Gaza's Kamal Adwan An Israeli drone attack on a hospital in northern Gaza injured staff members, despite a " ceasefire " being in place, according to the Palestinian Ministry of Health. At least three people were hurt in the courtyard and three others nearby, medical sources said. The attack happened despite the facility being in the so-called Green Zone, an area under Israeli control. The ministry condemned the attack, describing it as part of Israel's "systematic targeting of health facilities".

Robots available for rent: But what can they do? In hospitals across the US, patients and staff have become accustomed to seeing a one-armed, four-foot high, friendly-looking white robot going about its business. Nurses have been known to greet Moxi, as the robot is called by its maker Diligent Robotics, with a good morning, a high five or even a hug. Moxi - which shuttles medical supplies around hospitals - might respond by displaying its heart-shaped LED eyes and a beep beep greeting of its own. We get a lot of feedback that Moxi feels like a part of the team, says Todd Brugger, chief operating officer at the Texas-based robotics company, which has around 100 of the wheeled robots in operation.

Eli Lilly faces a congressional investigation into its China clinical trials at People's Liberation Army hospitals and Xinjiang facilities amid biotechnology competition concerns.

Venezuela earthquake rescuers discover collapsed buildings were'held up by STYROFOAM' as catastrophic death toll reaches 1,430 Remains of at least 117 dogs found at California'no-kill' shelter as investigators uncover suspected burial site and 600 collars Two young sisters smile for the camera as they're arrested for stabbing mom-of-five to death in broad daylight Sun-kissed enclave named Florida's newest boomtown as telltale restaurant chain opens more stores in area Horrifying truth about'squishy dumplings': Fears as putrid fumes'sicken' mom and toys explode in children's hands... experts sound alarm on possible cancer links in new analysis Delaware senator rushed to hospital after getting into car crash while sitting in passenger's seat World's first'pregnant man' Thomas Beatie reveals astonishing full story for the first time as his daughter turns 18... and confronts a hard truth about trans teens Taylor Swift gets BOOED at Alan Jackson's final concert... as Travis Kelce ...

AI is enabling a deluge of cyberattacks the likes of which we've never seen before. Late last month, I began to consider withdrawing some money from my savings account to buy gold. It's the first time I've ever thought about panic-buying. For all of the firewalls and two-factor-authentication codes, the safety of the internet is starting to falter. Hackers are gaining the upper hand over organizations around the world--hospitals, energy grids, government agencies, and, yes, banks.

Understanding how a prediction model will perform in a new environment before deployment is essential to preventing harm when algorithms inform decision-making. Two common sources of model performance degradation are (i) covariate shift, where the target covariate distribution differs from the source, and (ii) selective labels, where the observability of outcomes depends on historical decisions. We study pre-deployment model evaluation under the joint presence of covariate shift and labeling of outcomes selectively based on observed features. In particular, we present a double machine learning procedure for estimating the target risk of an arbitrary black-box prediction model under a general loss function. We show identification of this estimand under standard assumptions and derive a bias-corrected estimator based on the influence function of the target risk. Finally, we evaluate our estimator through experiments using the eICU electronic health records database, showing that it tracks the true target risk more accurately than methods that address either selective labels or covariate shift alone, as well as baselines that combine standard plug-in approaches.

Deploying clinical prediction models across healthcare systems often fails when key training covariates are unavailable at deployment and labeled outcomes are limited in the target domain. For example, high-performing models for out-of-hospital cardiac arrest (OHCA) rely on detailed prehospital measurements routinely collected in high-resource settings but unavailable in many international registries. Existing methods either discard missing covariates, sacrificing predictive information, or rely on untestable assumptions about their target distribution. We propose DRUM (\underline{D}istributionally \underline{R}obust \underline{U}nsupervised transfer learning with structurally \underline{M}issing covariates), a framework that transfers prediction models to target populations where certain covariates are structurally absent and outcome labels are unavailable. DRUM partitions covariates into shared components ($X$), observed across all settings, and missing components ($A$), observed only in the source. Rather than imputing missing covariates, DRUM optimizes worst-case predictive performance over the unknown target distribution of $A \mid X$ using a neural network generator, with a robustness parameter controlling allowable deviation from the source conditional. We further develop a bias correction procedure that reduces sensitivity to nuisance estimation error. Simulations show substantial improvements in both mean and worst-case prediction error under distribution shift. Applied to cross-national OHCA prediction, transferring models from a US registry to multiple Asian registries where prehospital variables are unrecorded, DRUM yields better-calibrated predictions and improved clinical classification performance across sites.

Who is James Murray, the new health secretary replacing Wes Streeting? From a high-profile, media-friendly Secretary of State to a relatively unknown MP, the departure of Wes Streeting and arrival of James Murray has raised eyebrows in the health and political worlds. It is one of the biggest Cabinet jobs with the largest public service departmental budgets. There will be a steep learning curve with no time for preparation away from the front line. Murray says he's deeply honoured to be appointed to the brief and continue Wes Streeting's brilliant work on such a critical mission, but who is he, and what issues will he face in his in tray?

'I was given a choice - keep my legs or keep my life' - the sepsis patient who lived Farmer Marshall Wylie thought nothing of it when he cut his arm, sorting wood in August 2023. And he thought even less of it when he felt ill over the next 48 hours. But the following week, he said he clinically died due to sepsis, and eventually his legs had to be amputated. Farmers are at particular risk of developing sepsis due to incidents on the farm, but can also be reluctant to seek healthcare. Warning: This article contains some graphic images of hands and feet with sepsis.