Hypnotherapy

Mice were anesthetized with a three compound mixture containing 0.5 mg/kg medetomidine, 5 mg/kg midazolam and 0.05 mg/kg fentanyl to achieve analgesia, amnesia and hypnosis prior to sham surgery or unilateral renal pedicle clamping with a micro aneurysm clamp (Medicon, Germany) via a flank incision. Ischemia times applied in this study were 15, 25, 35 and 45 minutes, following reperfusion. Online rectal temperature recording (ORTR) was installed for every mouse after the onset of complete anesthesia. For incision/ clamping and suturing mice were placed on a platform according to the protocol as shown in Fig 1A. After renal pedicle clamping and after clamp removal the successful reperfusion was assessed by color change from pale (ischemia) to the original color [19 (link)]. After clamping the kidney was placed back inside the abdomen, with only the clamps handle remaining outside the body. Sham-operated mice were handled in the same manner, except for clamping. After clamp removal wounds were closed using absorbable sutures for peritoneal and cutaneous layer (Ethicon, Belgium). For adjustment of fluid losses, 200 µl of saline were administered into the peritoneal cavity before wound closure. Anesthesia was antagonized using 2.5 mg/kg atipamezole, 0.5 mg/kg flumazenil and 1.2 mg/kg naloxone. 0.05 mg/kg Buprenorphin was injected subcutaneously for pain control. The experiments ended after 24 hours and 5 weeks of reperfusion time, respectively, when the animals were sacrificed by cervical dislocation and kidneys were collected. Post-ischemic and contralateral kidneys were removed, decapsulated, and divided, with one part being stored in 4% buffered formalin, and one part being frozen in RNAlater (Ambion, Germany).

A convenience sample [32 (link)] of UK resident adults who self-identified as having a formal diagnosis of IBS which had not responded to pharmacological treatments after 12 months and who had developed a continuing symptom profile [29 (link)] and who had never received hypnotherapy for their condition were recruited. Recruitment was via a poster campaign and by contacting IBS self-help groups and Facebook groups. No incentivisation was offered to potential interviewees but compensation for travel costs incurred in attending interviews was available. Both verbal and written consent for the interview were taken, in the case of video interviews verbal consent was obtained prior to the interview and confirmed in writing by post.
Interviews were conducted either in-person or face-to-face via video call. The decision to use mixed interview modes in this piece of research was taken whilst the study was ongoing and was in response to a sudden recruitment influx from internet advertising (Facebook). It was judged important to capitalise upon this influx rapidly due to the possibility of loss of interest by potential interviewees as the result of the time lag.
The transcription started from the point on the interview when the first question was asked by the interviewer. It concluded when the interviewer turned off the recording device, which was done when the answer to the last question was given and the interviewer judged that the interviewee had finished on the topic. Preamble and postamble were unrecorded. The interviewees perceptions of the interview process were not actively sought. Short pauses in speech were not recorded in the transcript, however if a pause was deemed to be unusually long or to denote a higher than average amount of thought an ellipse was inserted. Laughter and audible sighing were recorded with a single word within the transcript but no notes on body language were included.
A two-stage process of coding was undertaken. This process started with open coding [33 (link)]. Open coding involves a close read of the transcript to identify all statements, which are assigned a code. During open coding, codes are generated to fit the statements identified. For example, the statement “it’s got to the point that I know that whenever I’m eating out I know that I‘m going to swell” might generate the code ‘triggers for IBS’, and any subsequent statements regarding ‘triggers for IBS’ would then be assigned to this code. In this way 127 codes were generated. The second stage of the process was to reduce the codes by excluding any not relevant to the topics of interest, for example codes such as ‘non-IBS Life story’, and then amalgamating similar codes into a single code, so ‘massage’, ‘acupuncture’ and ‘meditation’ may all be combined under ‘complementary and alternative medicine (CAM)’. This left 79 codes. These transcripts were then coded again this time using the 79 codes only.
The same topic guide was used for both video call and in-person interviews. The same interviewer (MK) conducted and transcribed all the interviews. The idea to conduct the analysis of the two interview modes did not occur until after the coding had been undertaken. The full protocol of the study is available [26 (link)] as is the full source study [34 (link)].

To ensure that the driving environment generated by the simulation software was similar to that of the actual highway, a two-way four-lane highway scenario was selected for the simulated driving experiment in this paper. The whole road section was composed of straight road sections, with a total length of 40 km and a single lane width of 3.75 m. The scene that we designed in the simulated driving experiment was similar to the highway scene on the actual road. The road surface was smooth and open, without interference from pedestrians and other vehicles. There was no water, gravel, or other obstacles on the road surface, and there was no interference from buildings, bushes, pedestrians, or others on both sides of the road. Trees and grass on both sides of the road were added at regular intervals. This segment was repeated throughout the experiment until the end.
It was required that the subjects experienced no sleep disturbance, and, meanwhile, they were to ensure sufficient and regular sleep in the three days before the experiment. The degree of myopia of the experimenters did not exceed 600 degrees. The simulated driving test time was 9:00–11:00 in the morning. During the experiment, three experimental assistants were required. Before the simulated driving test began, the test assistants debugged the equipment. During the experiment, the subjects were required to wear eye trackers and drive in a single lane at a speed of 120 km/h for 20 min. Any problems during this process could be reported to the experimental assistants. A special experimental assistant was in charge of recording and observing the changes in the driver’s ECG in real time. They observed the driver’s external performance state when the driver’s ECG characteristics were stable. When the driver appeared in a state in which they might wake up from road hypnosis, the experimental assistant asked the driver whether the state occurred and recorded it. After the driving process was over, the subjects took off the eye tracker and rested for ten minutes, and the experimental assistants checked and debugged the equipment. During this period, an experimental assistant asked the subjects whether they had fatigue, distraction, and other behaviors during the driving process they had experienced and recorded them. Combining the whole video and the eye movement information video of the driver during the experiment, at the time point when road hypnosis may have appeared during the driving process, the driver was asked whether the road hypnosis occurred and it was recorded. After the break, the subjects wore eye trackers and kept driving in a single lane at a speed of 120 km/h. The driving time was 40 min under the condition without talking to other people. This process was consistent with the experimental process performed previously. After the simulated driving was completed, the experimental assistants organized the equipment and ended the experiment.

Based on the fact that the experimental data collected in this study were the data information with time series characteristics, the long short-term memory (LSTM) algorithm was used to establish a road hypnosis identification model. The structure of LSTM is shown in Figure 3.
LSTM mainly includes three gates, which are the input gate, forget gate, and output gate.
Input gate: The input gate is used to update the cell state. First, the information of the hidden state of the previous layer and the information of the current input are passed to the sigmoid function. We adjust the value within 0~1 to decide which information to update. Here, 0 means not important, and 1 means important. Secondly, the information of the hidden state of the previous layer and the information of the current input must be passed to the tanh function to create a new vector of candidate values. Finally, the output value of the sigmoid function is multiplied by the output value of the tanh function, and the output value of sigmoid will determine which information in the output value of the tanh function is important and needs to be preserved.
Forget gate: This determines which information should be discarded or kept. The information from the previous hidden state and the current input information are passed to the sigmoid function at the same time, and the output value is between 0 and 1. A closer value to 0 means that it should be discarded, and a value closer to 1 means that it should be retained.
Output gate: The output gate is used to determine the value of the next hidden state, which contains the information of the previous input. First, we pass the previous hidden state and the current input into the sigmoid function, and then pass the newly obtained cell state to the tanh function. Finally, the output of the tanh function is multiplied by the output of the sigmoid function to determine which information the hidden state should carry. Then, we use the hidden state as the output of the current cell, and pass the new cell state and new hidden state to the next time step.
The calculation formulas of the forget gate, input gate, and output gate are as follows: $$f_t=\sigma (W_f\cdot [h_{t-1},x_t]+b_f)$$
$f_t$ is the forget gate. $\sigma$ is the gate activation function. $h_{t-1}$ is the output at the previous time step (t − 1). $x_t$ is the current input. $W_f$ is the weight associated with $h_{t-1}$ and $x_t$ , while $b_f$ is the bias term.
$$\begin{array}{l}{\tilde{C}}_t=\tanh (W_C\cdot [h_{t-1},x_t]+b_C)\\ i_t=\sigma (W_i\cdot [h_{t-1},x_t]+b_i)\end{array}$$
$W_C$ and $W_i$ are the weights associated with $h_{t-1}$ and $x_t$ . $h_{t-1}$ is the output at the previous time step (t − 1). $x_t$ is the current input. $\sigma$ is the gate activation function. ${\tilde{C}}_t$ is the block input. $i_t$ is the input gate. $b_C$ and $b_i$ stand for the bias weight vector.
$$C_t=f_t{C}_{t-1}+i_t{\tilde{C}}_t$$
$C_t$ is the output gate. $f_t$ is the forget gate. $i_t$ is the input gate. ${\tilde{C}}_t$ is the block input, while $C_{t-1}$ is the block input at the previous time step (t − 1).
In addition, the problems regarding gradient disappearance and gradient explosion have also been properly solved in LSTM. Its calculation formula is as follows: $$\begin{array}{l}\frac{\partial C^{(k)}}{\partial C^{(k-1)}}=C^{(k-1)}{\sigma }^{\prime }(\cdot )W_f{o}^{(k-1)}{\tanh }^{\prime }(C^{(k-1)})+a^{(k)}{\sigma }^{\prime }(\cdot )W_i{o}^{(k-1)}{\tanh }^{\prime }(C^{(k-1)})\\ + i^{(k)}{\tanh }^{\prime }(\cdot )W_c{o}^{(k-1)}{\tanh }^{\prime }(C^{(k-1)})+f^{(t)}\\ {\displaystyle \prod _{k=t+1}^T\frac{\partial C^{(k)}}{\partial C^{(k-1)}}=(f^{(k)}{f}^{(k+1)}\cdots f^{(T)})}+other\end{array}$$